default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDJoão Gama 0001
João Manuel Portela da Gama
Person information
- affiliation: University of Porto, Laboratory of Artificial Intelligence and Decision Support (LIAAD), Portugal
Other persons with a similar name
- João Maurício Gama Boaventura
- João Monteiro 0004
![[0000-0002-3401-6495]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0002-3401-6495")
(aka: João Gama Monteiro 0004) — MARE, Marine and Environmental Sciences Centre, Aquatic Research Network, ARNET, Caniçal, Portugal - João Gabriel Gama Vila Nova
- João Gama Oliveira
SPARQL queries 
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[j136]Adrien Bécue, João Gama, Pedro Quelhas Brito:
AI's effect on innovation capacity in the context of industry 5.0: a scoping review. Artif. Intell. Rev. 57(8): 215 (2024)- [j135]Antonio R. Moya, Bruno Veloso, João Gama, Sebastián Ventura:
Improving hyper-parameter self-tuning for data streams by adapting an evolutionary approach. Data Min. Knowl. Discov. 38(3): 1289-1315 (2024) - [j134]Saulo Martiello Mastelini, Bruno Veloso, Max Halford, André Carlos Ponce de Leon Ferreira de Carvalho, João Gama:
SWINN: Efficient nearest neighbor search in sliding windows using graphs. Inf. Fusion 101: 101979 (2024) - [j133]Douglas Castilho, Thársis T. P. Souza, Soong Moon Kang, João Gama, André C. P. L. F. de Carvalho:
Forecasting financial market structure from network features using machine learning. Knowl. Inf. Syst. 66(8): 4497-4525 (2024) - [c235]César Andrade, Rita P. Ribeiro, João Gama:
Community-Based Topic Modeling with Contextual Outlier Handling. CAEPIA 2024: 173-183 - [c234]Matías Molina, Bruno Veloso, Carlos Abreu Ferreira, Rita P. Ribeiro, João Gama:
More (Enough) Is Better: Towards Few-Shot Illegal Landfill Waste Segmentation. ECAI 2024: 4547-4554 - [c233]Paula Raissa Silva, João Vinagre, João Gama:
Federated Online Learning for Heavy Hitter Detection. ECAI 2024: 4689-4695 - [c232]Amelia Zafra, Bruno Veloso, João Gama:
Early Failure Detection for Air Production Unit in Metro Trains. HAIS (1) 2024: 339-351 - [c231]Arijit Ukil, Angshul Majumdar, Antonio J. Jara, João Gama:
Deep Neural Network Model Compression and Signal Processing. ICASSP Workshops 2024: 179-183 - [c230]Pedro C. Vieira, João P. Montrezol, João T. Vieira, João Gama:
S+t-SNE - Bringing Dimensionality Reduction to Data Streams. IDA (2) 2024: 95-106 - [c229]Matías Molina, Rita P. Ribeiro, Bruno Veloso, João Gama:
Super-Resolution Analysis for Landfill Waste Classification. IDA (1) 2024: 155-166 - [c228]Narjes Davari, Bruno Veloso, Rita Paula Ribeiro, João Manuel Portela da Gama:
Detecting and Explaining Anomalies in the Air Production Unit of a Train. SAC 2024: 358-364 - [c227]Thiago Andrade, João Gama:
Where Do We Go From Here? Location Prediction from Time-Evolving Markov Models. SAC 2024: 365-367 - [c226]Maciej Mozolewski, Szymon Bobek, Rita P. Ribeiro, Grzegorz J. Nalepa, João Gama:
Towards Evaluation of Explainable Artificial Intelligence in Streaming Data. xAI (4) 2024: 145-168 - [i33]Teresa Salazar, João Gama, Helder Araújo, Pedro Henriques Abreu:
Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning. CoRR abs/2402.07586 (2024) - [i32]Pedro C. Vieira, João P. Montrezol, João T. Vieira, João Gama:
S+t-SNE - Bringing dimensionality reduction to data streams. CoRR abs/2403.17643 (2024) - [i31]Matías Molina, Rita P. Ribeiro, Bruno Veloso, João Gama:
Super-Resolution Analysis for Landfill Waste Classification. CoRR abs/2404.01790 (2024) - [i30]João Gama, Rita P. Ribeiro, Saulo Martiello Mastelini, Narjes Davari, Bruno Veloso:
A Neuro-Symbolic Explainer for Rare Events: A Case Study on Predictive Maintenance. CoRR abs/2404.14455 (2024) - [i29]Sérgio M. Jesus, Pedro Saleiro, Inês Oliveira e Silva, Beatriz M. Jorge, Rita P. Ribeiro, João Gama, Pedro Bizarro, Rayid Ghani:
Aequitas Flow: Streamlining Fair ML Experimentation. CoRR abs/2405.05809 (2024) - [i28]Jakub Jakubowski, Natalia Wojak-Strzelecka, Rita P. Ribeiro, Sepideh Pashami, Szymon Bobek, João Gama, Grzegorz J. Nalepa:
Artificial Intelligence Approaches for Predictive Maintenance in the Steel Industry: A Survey. CoRR abs/2405.12785 (2024) - [i27]Chongsheng Zhang, George Almpanidis, Gaojuan Fan, Binquan Deng, Yanbo Zhang, Ji Liu, Aouaidjia Kamel, Paolo Soda, João Gama:
A Systematic Review on Long-Tailed Learning. CoRR abs/2408.00483 (2024) - 2023
- [j132]Joel D. Costa, Elaine R. Faria, Jonathan de Andrade Silva, João Gama, Ricardo Cerri:
Novelty detection for multi-label stream classification under extreme verification latency. Appl. Soft Comput. 141: 110265 (2023) - [j131]Shazia Tabassum, João Gama, Paulo J. Azevedo, Mário Cordeiro, Carlos Martins, Andre Martins:
Social network analytics and visualization: Dynamic topic-based influence analysis in evolving micro-blogs. Expert Syst. J. Knowl. Eng. 40(5) (2023) - [j130]Jorge Meira, Bruno Veloso, Verónica Bolón-Canedo, Goreti Marreiros, Amparo Alonso-Betanzos, João Gama:
Data-driven predictive maintenance framework for railway systems. Intell. Data Anal. 27(4): 1087-1102 (2023) - [j129]Sofia Fernandes, Hadi Fanaee-T, João Gama, Leo Tisljaric, Tomislav Smuc:
WINTENDED: WINdowed TENsor decomposition for Densification Event Detection in time-evolving networks. Mach. Learn. 112(2): 459-481 (2023) - [j128]Jie Lu, João Gama, Xin Yao, Leandro L. Minku:
Guest Editorial: Special Issue on Stream Learning. IEEE Trans. Neural Networks Learn. Syst. 34(10): 6683-6685 (2023) - [j127]Paula Raissa Silva, João Vinagre, João Gama:
Towards federated learning: An overview of methods and applications. WIREs Data. Mining. Knowl. Discov. 13(2) (2023) - [c225]Arijit Ukil, João Gama, Antonio J. Jara, Leandro Marín:
Knowledge-driven Analytics and Systems Impacting Human Quality of Life- Neurosymbolic AI, Explainable AI and Beyond. CIKM 2023: 5296-5299 - [c224]Thiago Andrade, João Gama:
Which Way to Go - Finding Frequent Trajectories Through Clustering. DS 2023: 460-473 - [c223]Rafael Mamede, Nuno Paiva, João Gama:
Error Analysis on Industry Data: Using Weak Segment Detection for Local Model Agnostic Prediction Intervals. DS 2023: 661-672 - [c222]Thiago Andrade, Nirbhaya Shaji, Rita P. Ribeiro, João Gama:
Pollution Emission Patterns of Transportation in Porto, Portugal Through Network Analysis. EPIA (1) 2023: 215-226 - [c221]César Andrade, Rita P. Ribeiro, João Gama:
Topic Model with Contextual Outlier Handling: a Study on Electronic Invoice Product Descriptions. EPIA (1) 2023: 365-377 - [c220]Inês Martins, João S. Resende, João Gama:
Online Influence Forest for Streaming Anomaly Detection. IDA 2023: 274-286 - [c219]João Gama, Slawomir Nowaczyk, Sepideh Pashami, Rita P. Ribeiro, Grzegorz J. Nalepa, Bruno Veloso:
XAI for Predictive Maintenance. KDD 2023: 5798-5799 - [c218]Miguel E. P. Silva, Bruno Veloso, João Gama:
Predictive Maintenance, Adversarial Autoencoders and Explainability. ECML/PKDD (7) 2023: 260-275 - [c217]Paula Raissa Silva, João Vinagre, João Gama:
A DTW Approach for Complex Data A Case Study with Network Data Streams. SAC 2023: 402-409 - [c216]Thiago Andrade, João Gama:
Estimating Instantaneous Vehicle Emissions. SAC 2023: 422-424 - [c215]Szymon Bobek, Slawomir Nowaczyk, João Gama, Sepideh Pashami, Rita P. Ribeiro, Zahra Taghiyarrenani, Bruno Veloso, Lala H. Rajaoarisoa, Maciej Szelazek, Grzegorz J. Nalepa:
Why Industry 5.0 Needs XAI 2.0? xAI (Late-breaking Work, Demos, Doctoral Consortium) 2023: 1-6 - [e32]Albert Bifet, Ana Carolina Lorena, Rita P. Ribeiro, João Gama, Pedro H. Abreu:
Discovery Science - 26th International Conference, DS 2023, Porto, Portugal, October 9-11, 2023, Proceedings. Lecture Notes in Computer Science 14276, Springer 2023, ISBN 978-3-031-45274-1 [contents] - [e31]Irena Koprinska, Paolo Mignone, Riccardo Guidotti, Szymon Jaroszewicz, Holger Fröning, Francesco Gullo, Pedro M. Ferreira, Damian Roqueiro, Gaia Ceddia, Slawomir Nowaczyk, João Gama, Rita P. Ribeiro, Ricard Gavaldà, Elio Masciari, Zbigniew W. Ras, Ettore Ritacco, Francesca Naretto, Andreas Theissler, Przemyslaw Biecek, Wouter Verbeke, Gregor Schiele, Franz Pernkopf, Michaela Blott, Ilaria Bordino, Ivan Luciano Danesi, Giovanni Ponti, Lorenzo Severini, Annalisa Appice, Giuseppina Andresini, Ibéria Medeiros, Guilherme Graça, Lee A. D. Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Diego Saldana Miranda, Konstantinos Sechidis, Arif Canakoglu, Sara Pidò, Pietro Pinoli, Albert Bifet, Sepideh Pashami:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part I. Communications in Computer and Information Science 1752, Springer 2023, ISBN 978-3-031-23617-4 [contents] - [e30]Irena Koprinska, Paolo Mignone, Riccardo Guidotti, Szymon Jaroszewicz, Holger Fröning, Francesco Gullo, Pedro M. Ferreira, Damian Roqueiro, Gaia Ceddia, Slawomir Nowaczyk, João Gama, Rita P. Ribeiro, Ricard Gavaldà, Elio Masciari, Zbigniew W. Ras, Ettore Ritacco, Francesca Naretto, Andreas Theissler, Przemyslaw Biecek, Wouter Verbeke, Gregor Schiele, Franz Pernkopf, Michaela Blott, Ilaria Bordino, Ivan Luciano Danesi, Giovanni Ponti, Lorenzo Severini, Annalisa Appice, Giuseppina Andresini, Ibéria Medeiros, Guilherme Graça, Lee A. D. Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Diego Saldana Miranda, Konstantinos Sechidis, Arif Canakoglu, Sara Pidò, Pietro Pinoli, Albert Bifet, Sepideh Pashami:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part II. Communications in Computer and Information Science 1753, Springer 2023, ISBN 978-3-031-23632-7 [contents] - [d3]Narjes Davari, Bruno Veloso, Rita P. Ribeiro, João Gama:
MetroPT-3 Dataset. UCI Machine Learning Repository, 2023
- [i26]Angelica Liguori, Luciano Caroprese, Marco Minici, Bruno Veloso, Francesco Spinnato, Mirco Nanni, Giuseppe Manco, João Gama:
Modeling Events and Interactions through Temporal Processes - A Survey. CoRR abs/2303.06067 (2023) - [i25]Longbing Cao, Hui Chen, Xuhui Fan, João Gama, Yew-Soon Ong, Vipin Kumar:
Bayesian Federated Learning: A Survey. CoRR abs/2304.13267 (2023) - [i24]Sepideh Pashami, Slawomir Nowaczyk, Yuantao Fan, Jakub Jakubowski, Nuno Paiva, Narjes Davari, Szymon Bobek, Samaneh Jamshidi, Hamid Sarmadi, Abdallah Alabdallah, Rita P. Ribeiro, Bruno Veloso, Moamar Sayed Mouchaweh, Lala H. Rajaoarisoa, Grzegorz J. Nalepa, João Gama:
Explainable Predictive Maintenance. CoRR abs/2306.05120 (2023) - 2022
- [j126]João Gama, Rita P. Ribeiro, Bruno Veloso:
Data-Driven Predictive Maintenance. IEEE Intell. Syst. 37(4): 27-29 (2022) - [j125]Inês Martins, João S. Resende, Patrícia R. Sousa, Simão Silva, Luis Antunes, João Gama:
Host-based IDS: A review and open issues of an anomaly detection system in IoT. Future Gener. Comput. Syst. 133: 95-113 (2022) - [j124]Ana Rita Nogueira, Carlos Abreu Ferreira, João Gama:
Semi-causal decision trees. Prog. Artif. Intell. 11(1): 105-119 (2022) - [j123]Alexis Bondu, Youssef Achenchabe, Albert Bifet, Fabrice Clérot, Antoine Cornuéjols, João Gama, Georges Hébrail, Vincent Lemaire, Pierre-Francois Marteau:
Open challenges for Machine Learning based Early Decision-Making research. SIGKDD Explor. 24(2): 12-31 (2022) - [j122]Ana Rita Nogueira, Andrea Pugnana, Salvatore Ruggieri, Dino Pedreschi, João Gama:
Methods and tools for causal discovery and causal inference. WIREs Data Mining Knowl. Discov. 12(2) (2022) - [c214]Ana Rita Nogueira, Carlos Abreu Ferreira, João Gama:
Temporal Nodes Causal Discovery for in Intensive Care Unit Survival Analysis. EPIA 2022: 587-598 - [c213]Thiago Andrade, João Gama:
How are you Riding? Transportation Mode Identification from Raw GPS Data. EPIA 2022: 648-659 - [c212]Sónia Teixeira, José Rodrigues, Bruno Veloso, João Gama:
An Exploratory Diagnosis of Artificial Intelligence Risks for a Responsible Governance. ICEGOV 2022: 25-31 - [c211]Ricardo Cerri, Elaine R. Faria, João Gama:
An Algorithm Adaptation Method for Multi-Label Stream Classification using Self-Organizing Maps. ICMLA 2022: 1071-1076 - [c210]Narjes Davari, Sepideh Pashami, Bruno Veloso, Slawomir Nowaczyk, Yuantao Fan, Pedro Mota Pereira, Rita P. Ribeiro, João Gama:
A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data Set. IDA 2022: 39-52 - [c209]Nirbhaya Shaji, João Gama, Rita P. Ribeiro, Pedro Gomes:
Bank Statements to Network Features: Extracting Features Out of Time Series Using Visibility Graph. IDA 2022: 278-289 - [c208]Sérgio M. Jesus, José Pombal, Duarte Alves, André Ferreira Cruz, Pedro Saleiro, Rita P. Ribeiro, João Gama, Pedro Bizarro:
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation. NeurIPS 2022 - [c207]Rodrigo Salles, Jérôme Mendes, Rita P. Ribeiro, João Gama:
Fault Detection in Wastewater Treatment Plants: Application of Autoencoders Models with Streaming Data. PKDD/ECML Workshops (1) 2022: 55-70 - [c206]Sónia Teixeira, Bruno Veloso, José Coelho Rodrigues, João Gama:
Ethical and Technological AI Risks Classification: A Human Vs Machine Approach. PKDD/ECML Workshops (1) 2022: 150-166 - [c205]Nirbhaya Shaji, Thiago Andrade, Rita P. Ribeiro, João Gama:
Study on Correlation Between Vehicle Emissions and Air Quality in Porto. PKDD/ECML Workshops (1) 2022: 181-196 - [c204]Rita P. Ribeiro, Saulo Martiello Mastelini, Narjes Davari, Ehsan Aminian, Bruno Veloso, João Gama:
Online Anomaly Explanation: A Case Study on Predictive Maintenance. PKDD/ECML Workshops (2) 2022: 383-399 - [c203]Narjes Davari, Bruno Veloso, Rita P. Ribeiro, João Gama:
Fault Forecasting Using Data-Driven Modeling: A Case Study for Metro do Porto Data Set. PKDD/ECML Workshops (2) 2022: 400-409 - [c202]Emanuel Sousa Tomé, Rita P. Ribeiro, Bruno Veloso, João Gama:
An Online Data-Driven Predictive Maintenance Approach for Railway Switches. PKDD/ECML Workshops (2) 2022: 410-422 - [e29]João Gama, Tianrui Li, Yang Yu, Enhong Chen, Yu Zheng, Fei Teng:
Advances in Knowledge Discovery and Data Mining - 26th Pacific-Asia Conference, PAKDD 2022, Chengdu, China, May 16-19, 2022, Proceedings, Part I. Lecture Notes in Computer Science 13280, Springer 2022, ISBN 978-3-031-05932-2 [contents] - [e28]João Gama, Tianrui Li, Yang Yu, Enhong Chen, Yu Zheng, Fei Teng:
Advances in Knowledge Discovery and Data Mining - 26th Pacific-Asia Conference, PAKDD 2022, Chengdu, China, May 16-19, 2022, Proceedings, Part II. Lecture Notes in Computer Science 13281, Springer 2022, ISBN 978-3-031-05935-3 [contents] - [e27]João Gama, Tianrui Li, Yang Yu, Enhong Chen, Yu Zheng, Fei Teng:
Advances in Knowledge Discovery and Data Mining - 26th Pacific-Asia Conference, PAKDD 2022, Chengdu, China, May 16-19, 2022, Proceedings, Part III. Lecture Notes in Computer Science 13282, Springer 2022, ISBN 978-3-031-05980-3 [contents] - [d2]Bruno Veloso, João Gama, Rita P. Ribeiro, Pedro Pereira:
MetroPT: A Benchmark dataset for predictive maintenance. Version 1. Zenodo, 2022 [all versions] - [d1]Bruno Veloso, João Gama, Rita P. Ribeiro, Pedro Pereira:
MetroPT2: A Benchmark dataset for predictive maintenance. Version V2. Zenodo, 2022 [all versions] - [i23]Alexis Bondu, Youssef Achenchabe, Albert Bifet, Fabrice Clérot, Antoine Cornuéjols, João Gama, Georges Hébrail, Vincent Lemaire, Pierre-François Marteau:
Open challenges for Machine Learning based Early Decision-Making research. CoRR abs/2204.13111 (2022) - [i22]Paula Raissa Silva, João Vinagre, João Gama:
Federated Anomaly Detection over Distributed Data Streams. CoRR abs/2205.07829 (2022) - [i21]Rui Portocarrero Sarmento, Douglas de O. Cardoso, João Gama, Pavel Brazdil:
Contextualization for the Organization of Text Documents Streams. CoRR abs/2206.02632 (2022) - [i20]Bruno Veloso, João Gama, Rita P. Ribeiro, Pedro Mota Pereira:
A Benchmark dataset for predictive maintenance. CoRR abs/2207.05466 (2022) - [i19]Sérgio M. Jesus, José Pombal, Duarte Alves, André Ferreira Cruz, Pedro Saleiro, Rita P. Ribeiro, João Gama, Pedro Bizarro:
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation. CoRR abs/2211.13358 (2022) - [i18]Sónia Teixeira, José Rodrigues, Bruno Veloso, João Gama:
Humans Versus Machines: The Perspective of Two Different Approaches in Classification for Ethical Design. ERCIM News 2022(131) (2022) - 2021
- [j121]Sofia Fernandes, Hadi Fanaee-T, João Gama:
Tensor decomposition for analysing time-evolving social networks: an overview. Artif. Intell. Rev. 54(4): 2891-2916 (2021) - [j120]Adrien Bécue, Isabel Praça, João Gama:
Artificial intelligence, cyber-threats and Industry 4.0: challenges and opportunities. Artif. Intell. Rev. 54(5): 3849-3886 (2021) - [j119]Ehsan Aminian, Rita P. Ribeiro, João Gama:
Chebyshev approaches for imbalanced data streams regression models. Data Min. Knowl. Discov. 35(6): 2389-2466 (2021) - [j118]Paulo Paraíso, Saulo Ruiz, Pedro Gomes, Luís Rodrigues, João Gama:
Using network features for credit scoring in microfinance. Int. J. Data Sci. Anal. 12(2): 121-134 (2021) - [j117]Bruno Veloso, João Gama, Benedita Malheiro, João Vinagre:
Hyperparameter self-tuning for data streams. Inf. Fusion 76: 75-86 (2021) - [j116]Roberto Corizzo, Michelangelo Ceci, Hadi Fanaee-T, João Gama:
Multi-aspect renewable energy forecasting. Inf. Sci. 546: 701-722 (2021) - [j115]Narjes Davari, Bruno Veloso, Gustavo de Assis Costa, Pedro Mota Pereira, Rita P. Ribeiro, João Gama:
A Survey on Data-Driven Predictive Maintenance for the Railway Industry. Sensors 21(17): 5739 (2021) - [j114]Rui Portocarrero Sarmento, Douglas de O. Cardoso, Kemmily Dearo, Pavel Brazdil, João Gama:
Text documents streams with improved incremental similarity. Soc. Netw. Anal. Min. 11(1): 113 (2021) - [j113]João Vinagre, Alípio Mário Jorge, Conceição Rocha, João Gama:
Statistically Robust Evaluation of Stream-Based Recommender Systems. IEEE Trans. Knowl. Data Eng. 33(7): 2971-2982 (2021) - [j112]Maroua Bahri, Albert Bifet, João Gama, Heitor Murilo Gomes, Silviu Maniu:
Data stream analysis: Foundations, major tasks and tools. WIREs Data Mining Knowl. Discov. 11(3) (2021) - [c201]João Gama, Bruno Veloso, Ehsan Aminian, Rita P. Ribeiro:
Current Trends in Learning from Data Streams. BDA 2021: 183-193 - [c200]Jean-Gabriel Gaudreault, Paula Branco, João Gama:
An Analysis of Performance Metrics for Imbalanced Classification. DS 2021: 67-77 - [c199]Narjes Davari, Bruno Veloso, Rita P. Ribeiro, Pedro Mota Pereira, João Gama:
Predictive maintenance based on anomaly detection using deep learning for air production unit in the railway industry. DSAA 2021: 1-10 - [c198]Ana Rita Nogueira, Carlos Abreu Ferreira, João Gama, Alberto A. Pinto:
Generalised Partial Association in Causal Rules Discovery. EPIA 2021: 485-497 - [c197]Shazia Tabassum, João Gama, Paulo Azevedo, Luis Teixeira, Carlos Martins, Andre Martins:
Dynamic Topic Modeling Using Social Network Analytics. EPIA 2021: 498-509 - [c196]Patrício Costa, Ana Rita Nogueira, João Gama:
Modelling Voting Behaviour During a General Election Campaign Using Dynamic Bayesian Networks. EPIA 2021: 524-536 - [c195]Sérgio M. Jesus, Catarina G. Belém, Vladimir Balayan, João Bento, Pedro Saleiro, Pedro Bizarro, João Gama:
How can I choose an explainer?: An Application-grounded Evaluation of Post-hoc Explanations. FAccT 2021: 805-815 - [c194]Bruno Veloso, Luciano Caroprese, Matthias König, Sónia Teixeira, Giuseppe Manco, Holger H. Hoos, João Gama:
Hyper-parameter Optimization for Latent Spaces. ECML/PKDD (3) 2021: 249-264 - [c193]Sónia Teixeira, Guilherme Londres, Bruno Veloso, Rita P. Ribeiro, João Gama:
Improving Smart Waste Collection Using AutoML. PKDD/ECML Workshops (2) 2021: 283-298 - [c192]Ricardo Cerri, Joel David Costa Júnior, Elaine R. Faria, João Gama:
A new self-organizing map based algorithm for multi-label stream classification. SAC 2021: 418-426 - [e26]Pedro Henriques Abreu, Pedro Pereira Rodrigues, Alberto Fernández, João Gama:
Advances in Intelligent Data Analysis XIX - 19th International Symposium on Intelligent Data Analysis, IDA 2021, Porto, Portugal, April 26-28, 2021, Proceedings. Lecture Notes in Computer Science 12695, Springer 2021, ISBN 978-3-030-74250-8 [contents] - [e25]Michael Kamp, Irena Koprinska, Adrien Bibal, Tassadit Bouadi, Benoît Frénay, Luis Galárraga, José Oramas, Linara Adilova, Yamuna Krishnamurthy, Bo Kang, Christine Largeron, Jefrey Lijffijt, Tiphaine Viard, Pascal Welke, Massimiliano Ruocco, Erlend Aune, Claudio Gallicchio, Gregor Schiele, Franz Pernkopf, Michaela Blott, Holger Fröning, Günther Schindler, Riccardo Guidotti, Anna Monreale, Salvatore Rinzivillo, Przemyslaw Biecek, Eirini Ntoutsi, Mykola Pechenizkiy, Bodo Rosenhahn, Christopher L. Buckley, Daniela Cialfi, Pablo Lanillos, Maxwell Ramstead, Tim Verbelen, Pedro M. Ferreira, Giuseppina Andresini, Donato Malerba, Ibéria Medeiros, Philippe Fournier-Viger, M. Saqib Nawaz, Sebastián Ventura, Meng Sun, Min Zhou, Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Giovanni Ponti, Lorenzo Severini, Rita P. Ribeiro, João Gama, Ricard Gavaldà, Lee A. D. Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Damian Roqueiro, Diego Saldana Miranda, Konstantinos Sechidis, Guilherme Graça:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part I. Communications in Computer and Information Science 1524, Springer 2021, ISBN 978-3-030-93735-5 [contents] - [e24]Michael Kamp, Irena Koprinska, Adrien Bibal, Tassadit Bouadi, Benoît Frénay, Luis Galárraga, José Oramas, Linara Adilova, Yamuna Krishnamurthy, Bo Kang, Christine Largeron, Jefrey Lijffijt, Tiphaine Viard, Pascal Welke, Massimiliano Ruocco, Erlend Aune, Claudio Gallicchio, Gregor Schiele, Franz Pernkopf, Michaela Blott, Holger Fröning, Günther Schindler, Riccardo Guidotti, Anna Monreale, Salvatore Rinzivillo, Przemyslaw Biecek, Eirini Ntoutsi, Mykola Pechenizkiy, Bodo Rosenhahn, Christopher L. Buckley, Daniela Cialfi, Pablo Lanillos, Maxwell Ramstead, Tim Verbelen, Pedro M. Ferreira, Giuseppina Andresini, Donato Malerba, Ibéria Medeiros, Philippe Fournier-Viger, M. Saqib Nawaz, Sebastián Ventura, Meng Sun, Min Zhou, Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Giovanni Ponti, Lorenzo Severini, Rita P. Ribeiro, João Gama, Ricard Gavaldà, Lee A. D. Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Damian Roqueiro, Diego Saldana Miranda, Konstantinos Sechidis, Guilherme Graça:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part II. Communications in Computer and Information Science 1525, Springer 2021, ISBN 978-3-030-93732-4 [contents] - [i17]Sérgio M. Jesus, Catarina G. Belém, Vladimir Balayan, João Bento, Pedro Saleiro, Pedro Bizarro, João Gama:
How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations. CoRR abs/2101.08758 (2021) - [i16]Douglas Castilho, Thársis Tuani Pinto Souza, Soong Moon Kang, João Gama, André C. P. L. F. de Carvalho:
Forecasting Financial Market Structure from Network Features using Machine Learning. CoRR abs/2110.11751 (2021) - 2020
- [j111]Albert Bifet, João Gama:
IoT data stream analytics. Ann. des Télécommunications 75(9-10): 491-492 (2020) - [j110]Thiago Andrade, Brais Cancela, João Gama:
Discovering locations and habits from human mobility data. Ann. des Télécommunications 75(9-10): 505-521 (2020) - [j109]Shazia Tabassum, Muhammad Ajmal Azad, João Gama:
Profiling high leverage points for detecting anomalous users in telecom data networks. Ann. des Télécommunications 75(9-10): 573-581 (2020) - [j108]Bruno Veloso, Shazia Tabassum, Carlos Martins, Raphael Espanha, Raul Azevedo, João Gama:
Interconnect bypass fraud detection: a case study. Ann. des Télécommunications 75(9-10): 583-596 (2020) - [j107]Felipe Azevedo Pinage, Eulanda Miranda dos Santos, João Gama:
A drift detection method based on dynamic classifier selection. Data Min. Knowl. Discov. 34(1): 50-74 (2020) - [j106]Sofia Fernandes, Hadi Fanaee-T, João Gama:
NORMO: A new method for estimating the number of components in CP tensor decomposition. Eng. Appl. Artif. Intell. 96: 103926 (2020) - [j105]Thiago Andrade, Brais Cancela, João Gama:
From mobility data to habits and common pathways. Expert Syst. J. Knowl. Eng. 37(6) (2020) - [j104]Brais Cancela, Verónica Bolón-Canedo, Amparo Alonso-Betanzos, João Gama:
A scalable saliency-based feature selection method with instance-level information. Knowl. Based Syst. 192: 105326 (2020) - [j103]Shazia Tabassum, Bruno Veloso, João Gama:
On fast and scalable recurring link's prediction in evolving multi-graph streams. Netw. Sci. 8(S1): S65-S81 (2020) - [j102]Amal Saadallah, Luís Moreira-Matias, Ricardo Teixeira Sousa, Jihed Khiari, Erik Jenelius, João Gama:
BRIGHT - Drift-Aware Demand Predictions for Taxi Networks. IEEE Trans. Knowl. Data Eng. 32(2): 234-245 (2020) - [c191]Maroua Bahri, Bruno Veloso, Albert Bifet, João Gama:
AutoML for Stream k-Nearest Neighbors Classification. IEEE BigData 2020: 597-602 - [c190]Teru Fujii, Masahito Kumano, João Gama, Masahiro Kimura:
Detecting Geographical Competitive Structure for POI Visit Dynamics. COMPLEX NETWORKS (2) 2020: 27-38 - [c189]Saulo Ruiz, Pedro Gomes, Luís Rodrigues, João Gama:
Assembled Feature Selection for Credit Scoring in Microfinance with Non-traditional Features. DS 2020: 207-216 - [c188]Leo Tisljaric, Sofia Fernandes, Tonci Caric, João Gama:
Spatiotemporal Traffic Anomaly Detection on Urban Road Network Using Tensor Decomposition Method. DS 2020: 674-688 - [c187]Paulo Paraíso, Pedro Gomes, Saulo Ruiz, Luís Rodrigues, João Gama:
Using Network Features for Credit Scoring in MicroFinance: Extended Abstract. DSAA 2020: 783-784 - [c186]Ana Rita Nogueira, João Gama, Carlos Abreu Ferreira:
Improving Prediction with Causal Probabilistic Variables. IDA 2020: 379-390 - [c185]Bruno Veloso, Benedita Malheiro, Juan-Carlos Burguillo, João Gama:
Impact of Trust and Reputation Based Brokerage on the CloudAnchor Platform. PAAMS 2020: 303-314 - [c184]Bruno Veloso, João Gama:
Self Hyper-parameter Tuning for Stream Classification Algorithms. IoT Streams/ITEM@PKDD/ECML 2020: 3-13 - [c183]Mariana Barros, Bruno Veloso, Pedro Mota Pereira, Rita P. Ribeiro, João Gama:
Failure Detection of an Air Production Unit in Operational Context. IoT Streams/ITEM@PKDD/ECML 2020: 61-74 - [c182]Bruno Veloso, Carlos Martins, Raphael Espanha, Raul Azevedo, João Gama:
Fraud detection using heavy hitters: a case study. SAC 2020: 482-489 - [e23]Albert Bifet, Michele Berlingerio, João Gama, Jesse Read, Ana Rita Nogueira:
Proceedings of the 8th International Workshop on Big Data, IoT Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications co-located with 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), Anchorage, Alaska, August 4-8, 2019. CEUR Workshop Proceedings 2579, CEUR-WS.org 2020 [contents] - [e22]Irena Koprinska, Michael Kamp, Annalisa Appice, Corrado Loglisci, Luiza Antonie, Albrecht Zimmermann, Riccardo Guidotti, Özlem Özgöbek, Rita P. Ribeiro, Ricard Gavaldà, João Gama, Linara Adilova, Yamuna Krishnamurthy, Pedro M. Ferreira, Donato Malerba, Ibéria Medeiros, Michelangelo Ceci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras, Peter Christen, Eirini Ntoutsi, Erich Schubert, Arthur Zimek, Anna Monreale, Przemyslaw Biecek, Salvatore Rinzivillo, Benjamin Kille, Andreas Lommatzsch, Jon Atle Gulla:
ECML PKDD 2020 Workshops - Workshops of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020): SoGood 2020, PDFL 2020, MLCS 2020, NFMCP 2020, DINA 2020, EDML 2020, XKDD 2020 and INRA 2020, Ghent, Belgium, September 14-18, 2020, Proceedings. Communications in Computer and Information Science 1323, Springer 2020, ISBN 978-3-030-65964-6 [contents] - [e21]João Gama, Sepideh Pashami, Albert Bifet, Moamar Sayed Mouchaweh, Holger Fröning, Franz Pernkopf, Gregor Schiele, Michaela Blott:
IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning - Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Revised Selected Papers. Communications in Computer and Information Science 1325, Springer 2020, ISBN 978-3-030-66769-6 [contents] - [i15]Jie Lu, Anjin Liu, Fan Dong, Feng Gu, João Gama, Guangquan Zhang:
Learning under Concept Drift: A Review. CoRR abs/2004.05785 (2020) - [i14]Ricardo Cerri, Joel David Costa Júnior, Elaine Ribeiro de Faria Paiva, João Manuel Portela da Gama:
Multi-label Stream Classification with Self-Organizing Maps. CoRR abs/2004.09397 (2020) - [i13]Sónia Teixeira, João Gama, Pedro Amorim, Gonçalo Figueira:
Trustability in Algorithmic Systems Based on Artificial Intelligence in the Public and Private Sectors. ERCIM News 2020(122) (2020)
2010 – 2019
- 2019
- [j101]Guoliang Li, João Gama, Jun Yang:
Special Issue of DASFAA 2019. Data Sci. Eng. 4(3): 177-178 (2019) - [j100]Eduarda Portela, Rita P. Ribeiro, João Gama:
The search of conditional outliers. Intell. Data Anal. 23(1): 23-39 (2019) - [j99]Saulo Ruiz, Pedro Gomes, Luís Rodrigues, João Gama:
Credit scoring for microfinance using behavioral data in emerging markets. Intell. Data Anal. 23(6): 1355-1378 (2019) - [j98]Fabíola Souza Fernandes Pereira, Claudio D. G. Linhares, Jean R. Ponciano, João Gama, Sandra de Amo, Gina M. B. Oliveira:
Uma Análise sobre a Evolução das Preferências Musicais dos Usuários Utilizando Redes de Similaridade Temporal. Braz. J. Inf. Syst. 12(3): 94-115 (2019) - [j97]Jakub Zgraja, Richard Hugh Moulton, João Gama, Andrzej Kasprzak, Michal Wozniak:
Adapting ClusTree for more challenging data stream environments. J. Intell. Fuzzy Syst. 37(6): 7679-7688 (2019) - [j96]Carlos Abreu Ferreira, João Gama, Vítor Santos Costa:
Contrasting logical sequences in multi-relational learning. Prog. Artif. Intell. 8(4): 487-503 (2019) - [j95]Wesllen Sousa Lima, Eduardo Souto, Khalil El-Khatib, Roozbeh Jalali, João Gama:
Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview. Sensors 19(14): 3213 (2019) - [j94]Heitor Murilo Gomes, Jesse Read, Albert Bifet, Jean Paul Barddal, João Gama:
Machine learning for streaming data: state of the art, challenges, and opportunities. SIGKDD Explor. 21(2): 6-22 (2019) - [j93]Jie Lu, Anjin Liu, Fan Dong, Feng Gu, João Gama, Guangquan Zhang:
Learning under Concept Drift: A Review. IEEE Trans. Knowl. Data Eng. 31(12): 2346-2363 (2019) - [c181]Joel D. Costa Júnior, Elaine Ribeiro de Faria, Jonathan de Andrade Silva, João Gama, Ricardo Cerri:
Novelty Detection for Multi-Label Stream Classification. BRACIS 2019: 144-149 - [c180]Mário Cordeiro, Rui Portocarrero Sarmento, Pavel Brazdil, Masahiro Kimura, João Gama:
Identifying, Ranking and Tracking Community Leaders in Evolving Social Networks. COMPLEX NETWORKS (1) 2019: 198-210 - [c179]Andreia Conceição, João Gama:
Main Factors Driving the Open Rate of Email Marketing Campaigns. DS 2019: 145-154 - [c178]Sofia Fernandes, Hadi Fanaee-T, João Gama:
Evolving Social Networks Analysis via Tensor Decompositions: From Global Event Detection Towards Local Pattern Discovery and Specification. DS 2019: 385-395 - [c177]Thiago Andrade, Brais Cancela, João Gama:
Discovering Common Pathways Across Users' Habits in Mobility Data. EPIA (2) 2019: 410-421 - [c176]Douglas Castilho, João Gama, Leandro Resende Mundim, André C. P. L. F. de Carvalho:
Improving Portfolio Optimization Using Weighted Link Prediction in Dynamic Stock Networks. ICCS (3) 2019: 340-353 - [c175]Amal Saadallah, Luís Moreira-Matias, Ricardo Teixeira Sousa, Jihed Khiari, Erik Jenelius, João Gama:
BRIGHT - Drift-Aware Demand Predictions for Taxi Networks (Extended Abstract). ICDE 2019: 2145-2146 - [c174]Joel D. Costa Júnior, Elaine R. Faria, Jonathan de Andrade Silva, João Gama, Ricardo Cerri:
Pruned Sets for Multi-Label Stream Classification without True Labels. IJCNN 2019: 1-8 - [c173]Bruno Veloso, Carlos Martins, Raphael Espanha, Raul Azevedo, João Gama:
Detecting Bursts of Activity in Telecommunications. BigMine@KDD 2019 - [c172]Ehsan Aminian, Rita P. Ribeiro, João Gama:
A Study on Imbalanced Data Streams. PKDD/ECML Workshops (2) 2019: 380-389 - [c171]Thiago Andrade, Brais Cancela, João Gama:
Mining Human Mobility Data to Discover Locations and Habits. PKDD/ECML Workshops (2) 2019: 390-401 - [c170]Guilherme Londres, Nuno Filipe, João Gama:
Optimizing Waste Collection: A Data Mining Approach. PKDD/ECML Workshops (1) 2019: 570-578 - [e20]Guoliang Li, Jun Yang, João Gama, Juggapong Natwichai, Yongxin Tong:
Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Chiang Mai, Thailand, April 22-25, 2019, Proceedings, Part I. Lecture Notes in Computer Science 11446, Springer 2019, ISBN 978-3-030-18575-6 [contents] - [e19]Guoliang Li, Jun Yang, João Gama, Juggapong Natwichai, Yongxin Tong:
Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Chiang Mai, Thailand, April 22-25, 2019, Proceedings, Part II. Lecture Notes in Computer Science 11447, Springer 2019, ISBN 978-3-030-18578-7 [contents] - [e18]Guoliang Li, Jun Yang, João Gama, Juggapong Natwichai, Yongxin Tong:
Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Chiang Mai, Thailand, April 22-25, 2019, Proceedings, Part III, and DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Chiang Mai, Thailand, April 22-25, 2019, Proceedings. Lecture Notes in Computer Science 11448, Springer 2019, ISBN 978-3-030-18589-3 [contents] - [e17]Anna Monreale, Carlos Alzate, Michael Kamp, Yamuna Krishnamurthy, Daniel Paurat, Moamar Sayed Mouchaweh, Albert Bifet, João Gama, Rita P. Ribeiro:
ECML PKDD 2018 Workshops - DMLE 2018 and IoTStream 2018, Dublin, Ireland, September 10-14, 2018, Revised Selected Papers. Communications in Computer and Information Science 967, Springer 2019, ISBN 978-3-030-14879-9 [contents] - [i12]Thiago Andrade, João Gama:
Identifying Points of Interest and Similar Individuals from Raw GPS Data. CoRR abs/1904.09357 (2019) - [i11]Brais Cancela, Verónica Bolón-Canedo, Amparo Alonso-Betanzos, João Gama:
A scalable saliency-based Feature selection method with instance level information. CoRR abs/1904.13127 (2019) - [i10]Richard Hugh Moulton, Herna L. Viktor, Nathalie Japkowicz, João Gama:
Contextual One-Class Classification in Data Streams. CoRR abs/1907.04233 (2019) - [i9]Thiago Andrade, Brais Cancela, João Gama:
Mining Human Mobility Data to Discover Locations and Habits. CoRR abs/1909.11406 (2019) - 2018
- [j92]Sofia Fernandes, Hadi Fanaee-T, João Gama:
Dynamic graph summarization: a tensor decomposition approach. Data Min. Knowl. Discov. 32(5): 1397-1420 (2018) - [j91]João Vinagre, Alípio Mário Jorge, João Gama:
Online bagging for recommender systems. Expert Syst. J. Knowl. Eng. 35(4) (2018) - [j90]Ana Rita Nogueira, Carlos Abreu Ferreira, João Gama:
Improving acute kidney injury detection with conditional probabilities. Intell. Data Anal. 22(6): 1355-1374 (2018) - [j89]Pedro Pereira Rodrigues, João Araújo, João Gama, Luís M. B. Lopes:
A local algorithm to approximate the global clustering of streams generated in ubiquitous sensor networks. Int. J. Distributed Sens. Networks 14(10) (2018) - [j88]Pawel Matuszyk, João Vinagre, Myra Spiliopoulou, Alípio Mário Jorge, João Gama:
Forgetting techniques for stream-based matrix factorization in recommender systems. Knowl. Inf. Syst. 55(2): 275-304 (2018) - [j87]Juan Gabriel Colonna, João Gama, Eduardo Freire Nakamura:
A comparison of hierarchical multi-output recognition approaches for anuran classification. Mach. Learn. 107(11): 1651-1671 (2018) - [j86]Fabíola Souza F. Pereira, João Gama, Sandra de Amo, Gina M. B. Oliveira:
On analyzing user preference dynamics with temporal social networks. Mach. Learn. 107(11): 1745-1773 (2018) - [j85]Ricardo Teixeira Sousa, João Gama:
Multi-label classification from high-speed data streams with adaptive model rules and random rules. Prog. Artif. Intell. 7(3): 177-187 (2018) - [j84]Luís Moreira-Matias, João Manuel Portela da Gama, Cristina Olaverri-Monreal, Rahul Nair, Roberto Trasarti:
Guest Editorial Special Issue on Knowledge Discovery From Mobility Data for Intelligent Transportation Systems. IEEE Trans. Intell. Transp. Syst. 19(11): 3626-3629 (2018) - [j83]Shazia Tabassum, Fabíola Souza F. Pereira, Sofia Fernandes, João Gama:
Cover Image, Volume 8, Issue 5. WIREs Data Mining Knowl. Discov. 8(5) (2018) - [j82]Shazia Tabassum, Fabíola Souza F. Pereira, Sofia Fernandes, João Gama:
Social network analysis: An overview. WIREs Data Mining Knowl. Discov. 8(5) (2018) - [c169]Carlos Fernandes, Luis Fonseca, Flora J. Ferreira, Miguel F. Gago, Luís Costa, Nuno J. Sousa, Carlos Abreu Ferreira, João Gama, Wolfram Erlhagen, Estela Bicho:
Artificial Neural Networks Classification of Patients with Parkinsonism based on Gait. BIBM 2018: 2024-2030 - [c168]Shazia Tabassum, João Gama:
Biased Dynamic Sampling for Temporal Network Streams. COMPLEX NETWORKS (1) 2018: 512-523 - [c167]João Vinagre, Alípio Mário Jorge, João Gama:
Online Gradient Boosting for Incremental Recommender Systems. DS 2018: 209-223 - [c166]Bruno Veloso, João Gama, Benedita Malheiro:
Self Hyper-Parameter Tuning for Data Streams. DS 2018: 241-255 - [c165]Rui Portocarrero Sarmento, Mário Cordeiro, Pavel Brazdil, João Gama:
Incremental TextRank - Automatic Keyword Extraction for Text Streams. ICEIS (1) 2018: 363-370 - [c164]Jakub Zgraja, João Gama, Michal Wozniak:
Active Learning by Clustering for Drifted Data Stream Classification. DMLE/IOTSTREAMING@PKDD/ECML 2018: 80-90 - [c163]Bruno Veloso, João Gama, Benedita Malheiro, João Vinagre:
Self Hyper-parameter Tuning for Stream Recommendation Algorithms. DMLE/IOTSTREAMING@PKDD/ECML 2018: 91-102 - [c162]Richard Hugh Moulton, Herna L. Viktor, Nathalie Japkowicz, João Gama:
Clustering in the Presence of Concept Drift. ECML/PKDD (1) 2018: 339-355 - [c161]Ricardo Teixeira Sousa, João Gama:
Co-training study for online regression. SAC 2018: 529-531 - [c160]Bruno Veloso, Benedita Malheiro, Juan-Carlos Burguillo, Jeremy D. Foss, João Gama:
Personalised Dynamic Viewer Profiling for Streamed Data. WorldCIST (2) 2018: 501-510 - [e16]M. Arif Wani, Mehmed M. Kantardzic, Moamar Sayed Mouchaweh, João Gama, Edwin Lughofer:
17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, Orlando, FL, USA, December 17-20, 2018. IEEE 2018, ISBN 978-1-5386-6805-4 [contents] - [e15]Moamar Sayed Mouchaweh, Hamid Bouchachia, João Gama, Rita Paula Ribeiro:
Proceedings of the Workshop on Large-scale Learning from Data Streams in Evolving Environments (STREAMEVOLV 2016) co-located with the 2016 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2016), Riva del Garda, Italy, September 23, 2016. CEUR Workshop Proceedings 2069, CEUR-WS.org 2018 [contents] - [i8]Mário Cordeiro, Rui Portocarrero Sarmento, Pavel Brazdil, João Gama:
Dynamic Laplace: Efficient Centrality Measure for Weighted or Unweighted Evolving Networks. CoRR abs/1808.02960 (2018) - 2017
- [j81]Jonathan de Andrade Silva, Eduardo Raul Hruschka, João Gama:
An evolutionary algorithm for clustering data streams with a variable number of clusters. Expert Syst. Appl. 67: 228-238 (2017) - [j80]Raquel Sebastião, João Gama, Teresa Mendonça:
Fading histograms in detecting distribution and concept changes. Int. J. Data Sci. Anal. 3(3): 183-212 (2017) - [j79]Bartosz Krawczyk, Leandro L. Minku, João Gama, Jerzy Stefanowski, Michal Wozniak:
Ensemble learning for data stream analysis: A survey. Inf. Fusion 37: 132-156 (2017) - [j78]Douglas de O. Cardoso, João Gama, Felipe M. G. França:
Weightless neural networks for open set recognition. Mach. Learn. 106(9-10): 1547-1567 (2017) - [j77]João Gama, Eugénio C. Oliveira, Henrique Lopes Cardoso:
Computational Models for Social and Technical Interactions. New Gener. Comput. 35(4): 307-310 (2017) - [j76]Douglas de O. Cardoso, Felipe M. G. França, João Gama:
WCDS: A Two-Phase Weightless Neural System for Data Stream Clustering. New Gener. Comput. 35(4): 391-416 (2017) - [c159]Rui Portocarrero Sarmento, Mário Cordeiro, Pavel Brazdil, João Gama:
Efficient Incremental Laplace Centrality Algorithm for Dynamic Networks. COMPLEX NETWORKS 2017: 341-352 - [c158]Sofia Fernandes, Hadi Fanaee Tork, João Manuel Portela da Gama:
The Initialization and Parameter Setting Problem in Tensor Decomposition-Based Link Prediction. DSAA 2017: 99-108 - [c157]Hamid Eslami Nosratabadi, Hadi Fanaee-T, João Gama:
Mobility Mining Using Nonnegative Tensor Factorization. EPIA 2017: 321-330 - [c156]Saulo Ruiz, Pedro Gomes, Luís Rodrigues, João Gama:
Credit Scoring in Microfinance Using Non-traditional Data. EPIA 2017: 447-458 - [c155]João Vinagre, Alípio Mário Jorge, João Gama:
Improving Incremental Recommenders with Online Bagging. EPIA 2017: 597-607 - [c154]Ana Rita Nogueira, Carlos Abreu Ferreira, João Gama:
Acute Kidney Injury Detection: An Alarm System to Improve Early Treatment. ISMIS 2017: 57-63 - [c153]Ricardo Teixeira Sousa, João Gama:
Co-training Semi-supervised Learning for Single-Target Regression in Data Streams Using AMRules. ISMIS 2017: 499-508 - [c152]Eduarda Portela, Rita P. Ribeiro, João Gama:
Outliers and the Simpson's Paradox. MICAI (1) 2017: 267-278 - [c151]Ricardo Teixeira Sousa, João Gama:
Comparison Between Co-training and Self-training for Single-target Regression in Data Streams using AMRules. IOTSTREAMING@PKDD/ECML 2017 - [c150]João Duarte, João Gama:
Feature ranking in hoeffding algorithms for regression. SAC 2017: 836-841 - [e14]Eugénio C. Oliveira, João Gama, Zita A. Vale, Henrique Lopes Cardoso:
Progress in Artificial Intelligence - 18th EPIA Conference on Artificial Intelligence, EPIA 2017, Porto, Portugal, September 5-8, 2017, Proceedings. Lecture Notes in Computer Science 10423, Springer 2017, ISBN 978-3-319-65339-6 [contents] - [e13]Ricard Gavaldà, Indre Zliobaite, João Gama:
Proceedings of the First Workshop on Data Science for Social Good co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Dicovery in Databases, SoGood@ECML-PKDD 2016, Riva del Garda, Italy, September 19, 2016. CEUR Workshop Proceedings 1831, CEUR-WS.org 2017 [contents] - [e12]Moamar Sayed Mouchaweh, Albert Bifet, Hamid Bouchachia, João Gama, Rita Paula Ribeiro:
Proceedings of the Workshop on IoT Large Scale Learning from Data Streams co-located with the 2017 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2017), Skopje, Macedonia, September 18-22, 2017. CEUR Workshop Proceedings 1958, CEUR-WS.org 2017 [contents] - [r4]João Gama:
Clustering from Data Streams. Encyclopedia of Machine Learning and Data Mining 2017: 226-231 - 2016
- [j75]Elaine R. Faria, Isabel J. C. R. Gonçalves, André C. P. L. F. de Carvalho, João Gama:
Novelty detection in data streams. Artif. Intell. Rev. 45(2): 235-269 (2016) - [j74]Luís Moreira-Matias, Oded Cats, João Gama, João Mendes-Moreira, Jorge Freire de Sousa:
An online learning approach to eliminate Bus Bunching in real-time. Appl. Soft Comput. 47: 460-482 (2016) - [j73]F. E. Correa, Márcia D. B. Oliveira, João Gama, Pedro Luiz Pizzigatti Corrêa, Jorge Rady de Almeida Jr.:
Analyzing the behavior dynamics of grain price indexes using Tucker tensor decomposition and spatio-temporal trajectories. Comput. Electron. Agric. 120: 72-78 (2016) - [j72]Elaine Ribeiro de Faria, André Carlos Ponce de Leon Ferreira de Carvalho, João Gama:
MINAS: multiclass learning algorithm for novelty detection in data streams. Data Min. Knowl. Discov. 30(3): 640-680 (2016) - [j71]Luís Moreira-Matias, João Gama, Michel Ferreira, João Mendes-Moreira, Luís Damas:
Time-evolving O-D matrix estimation using high-speed GPS data streams. Expert Syst. Appl. 44: 275-288 (2016) - [j70]Maria Rocha Sousa, João Gama, Elísio Brandão:
A new dynamic modeling framework for credit risk assessment. Expert Syst. Appl. 45: 341-351 (2016) - [j69]Hanen Borchani, Pedro Larrañaga, João Gama, Concha Bielza:
Mining multi-dimensional concept-drifting data streams using Bayesian network classifiers. Intell. Data Anal. 20(2): 257-280 (2016) - [j68]Hadi Fanaee-T, João Gama:
Event detection from traffic tensors: A hybrid model. Neurocomputing 203: 22-33 (2016) - [j67]Hadi Fanaee-T, João Gama:
Tensor-based anomaly detection: An interdisciplinary survey. Knowl. Based Syst. 98: 130-147 (2016) - [j66]Rita P. Ribeiro, Pedro Mota Pereira, João Gama:
Sequential anomalies: a study in the Railway Industry. Mach. Learn. 105(1): 127-153 (2016) - [j65]Mário Cordeiro, Rui Sarmento, João Gama:
Dynamic community detection in evolving networks using locality modularity optimization. Soc. Netw. Anal. Min. 6(1): 15:1-15:20 (2016) - [j64]João Duarte, João Gama, Albert Bifet:
Adaptive Model Rules From High-Speed Data Streams. ACM Trans. Knowl. Discov. Data 10(3): 30:1-30:22 (2016) - [j63]Felipe Azevedo Pinage, Eulanda Miranda dos Santos, João Manuel Portela da Gama:
Classification systems in dynamic environments: an overview. WIREs Data Mining Knowl. Discov. 6(5): 156-166 (2016) - [c149]Mário Cordeiro, João Gama:
Online Social Networks Event Detection: A Survey. Solving Large Scale Learning Tasks 2016: 1-41 - [c148]Juan Gabriel Colonna, Tanel Peet, Carlos Abreu Ferreira, Alípio Mário Jorge, Elsa Ferreira Gomes, João Gama:
Automatic Classification of Anuran Sounds Using Convolutional Neural Networks. C3S2E 2016: 73-78 - [c147]Juan Gabriel Colonna, João Gama, Eduardo Freire Nakamura:
How to Correctly Evaluate an Automatic Bioacoustics Classification Method. CAEPIA 2016: 37-47 - [c146]Ricardo Teixeira Sousa, João Gama:
Online Multi-label Classification with Adaptive Model Rules. CAEPIA 2016: 58-67 - [c145]Juan Gabriel Colonna, João Gama, Eduardo Freire Nakamura:
Recognizing Family, Genus, and Species of Anuran Using a Hierarchical Classification Approach. DS 2016: 198-212 - [c144]Shazia Tabassum, João Gama:
Evolution Analysis of Call Ego-Networks. DS 2016: 213-225 - [c143]Fabíola Souza F. Pereira, Sandra de Amo, João Gama:
On Using Temporal Networks to Analyze User Preferences Dynamics. DS 2016: 408-423 - [c142]Alípio M. Jorge, João Vinagre, Marcos Aurélio Domingues, João Gama, Carlos Soares, Pawel Matuszyk, Myra Spiliopoulou:
Scalable Online Top-N Recommender Systems. EC-Web 2016: 3-20 - [c141]Rita P. Ribeiro, Ricardo Oliveira, João Gama:
Detection of Fraud Symptoms in the Retail Industry. IBERAMIA 2016: 189-200 - [c140]Ricardo Teixeira Sousa, João Gama:
Online Semi-supervised Learning for Multi-target Regression in Data Streams Using AMRules. IDA 2016: 123-133 - [c139]Gianmarco De Francisci Morales, Albert Bifet, Latifur Khan, João Gama, Wei Fan:
IoT Big Data Stream Mining. KDD 2016: 2119-2120 - [c138]Fabíola Souza F. Pereira, Sandra de Amo, João Gama:
Evolving Centralities in Temporal Graphs: A Twitter Network Analysis. MDM (Workshops) 2016: 43-48 - [c137]Shazia Tabassum, João Gama:
Sampling Evolving Ego-Networks with forgetting Factor. MDM (Workshops) 2016: 55-59 - [c136]Luís Moreira-Matias, João Gama, João Mendes-Moreira:
Concept Neurons - Handling Drift Issues for Real-Time Industrial Data Mining. ECML/PKDD (3) 2016: 96-111 - [c135]Fabíola Souza F. Pereira, Sandra de Amo, João Gama:
Detecting Events in Evolving Social Networks through Node Centrality Analysis. STREAMEVOLV@ECML-PKDD 2016 - [c134]Ricardo Teixeira Sousa, João Gama:
First Principle Models Based Dataset Generation for Multi-Target Regression and Multi-Label Classification Evaluation. STREAMEVOLV@ECML-PKDD 2016 - [c133]João Vinagre, Alípio Mário Jorge, João Gama:
Online Bagging for Recommendation with Incremental Matrix Factorization. STREAMEVOLV@ECML-PKDD 2016 - [c132]Shazia Tabassum, João Gama:
Sampling massive streaming call graphs. SAC 2016: 923-928 - [c131]Douglas de O. Cardoso, Felipe Maia Galvão França, João Gama:
Clustering data streams using a forgetful neural model. SAC 2016: 949-951 - [i7]João Vinagre, Alípio Mário Jorge, João Gama:
Online bagging for recommendation with incremental matrix factorization. CoRR abs/1611.00558 (2016) - [i6]Hadi Fanaee-T, João Gama:
SimTensor: A synthetic tensor data generator. CoRR abs/1612.03772 (2016) - 2015
- [j62]Petr Kosina, João Gama:
Very fast decision rules for classification in data streams. Data Min. Knowl. Discov. 29(1): 168-202 (2015) - [j61]Carlos Sáez, Pedro Pereira Rodrigues, João Gama, Montserrat Robles, Juan Miguel García-Gómez:
Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality. Data Min. Knowl. Discov. 29(4): 950-975 (2015) - [j60]Concha Bielza, João Gama, Alípio Jorge, Indre Zliobaite:
Guest editors introduction: special issue of the ECMLPKDD 2015 journal track. Data Min. Knowl. Discov. 29(5): 1113-1115 (2015) - [j59]Hadi Fanaee-T, João Gama:
Eigenspace method for spatiotemporal hotspot detection. Expert Syst. J. Knowl. Eng. 32(3): 454-464 (2015) - [j58]Hadi Fanaee-T, João Gama:
EigenEvent: An algorithm for event detection from complex data streams in syndromic surveillance. Intell. Data Anal. 19(3): 597-616 (2015) - [j57]Elena Ikonomovska, João Gama, Saso Dzeroski:
Online tree-based ensembles and option trees for regression on evolving data streams. Neurocomputing 150: 458-470 (2015) - [j56]João Mendes-Moreira, Luís Moreira-Matias, João Gama, Jorge Freire de Sousa:
Validating the coverage of bus schedules: A Machine Learning approach. Inf. Sci. 293: 299-313 (2015) - [j55]Hadi Fanaee-T, João Gama:
Multi-aspect-streaming tensor analysis. Knowl. Based Syst. 89: 332-345 (2015) - [j54]Concha Bielza, João Gama, Alípio Jorge, Indre Zliobaite:
Guest Editors introduction: special issue of the ECMLPKDD 2015 journal track. Mach. Learn. 100(2-3): 157-159 (2015) - [j53]Carlos Abreu Ferreira, João Gama, Vítor Santos Costa:
Exploring multi-relational temporal databases with a propositional sequence miner. Prog. Artif. Intell. 4(1-2): 11-20 (2015) - [j52]Luís Moreira-Matias, João Mendes-Moreira, Jorge Freire de Sousa, João Gama:
Improving Mass Transit Operations by Using AVL-Based Systems: A Survey. IEEE Trans. Intell. Transp. Syst. 16(4): 1636-1653 (2015) - [j51]Elaine Ribeiro de Faria, Isabel Ribeiro Gonçalves, João Gama, André Carlos Ponce de Leon Ferreira de Carvalho:
Evaluation of Multiclass Novelty Detection Algorithms for Data Streams. IEEE Trans. Knowl. Data Eng. 27(11): 2961-2973 (2015) - [j50]João Vinagre, Alípio Mário Jorge, João Gama:
An overview on the exploitation of time in collaborative filtering. WIREs Data Mining Knowl. Discov. 5(5): 195-215 (2015) - [c130]Vânia Almeida, João Gama:
Measures for Combining Prediction Intervals Uncertainty and Reliability in Forecasting. CORES 2015: 147-157 - [c129]João Duarte, João Gama:
Multi-target regression from high-speed data streams with adaptive model rules. DSAA 2015: 1-10 - [c128]João Gama:
Keynote speaker 2: Real time data mining. EAIS 2015: 1 - [c127]Ana M. Silva, Rita P. Ribeiro, João Gama:
An Experimental Study on Predictive Models Using Hierarchical Time Series. EPIA 2015: 501-512 - [c126]Rui Sarmento, Mário Cordeiro, João Gama:
Streaming Networks Sampling using top-K Networks. ICEIS (1) 2015: 228-234 - [c125]Vítor Cerqueira, Márcia D. B. Oliveira, João Gama:
A Framework for Analysing Dynamic Communities in Large-scale Social Networks. ICEIS (1) 2015: 235-242 - [c124]Vinícius M. A. de Souza, Diego Furtado Silva, Gustavo E. A. P. A. Batista, João Gama:
Classification of Evolving Data Streams with Infinitely Delayed Labels. ICMLA 2015: 214-219 - [c123]Douglas de O. Cardoso, Felipe Maia Galvão França, João Gama:
A bounded neural network for open set recognition. IJCNN 2015: 1-7 - [c122]Yusuke Sakamoto, Ken-ichi Fukui, João Gama, Daniela Nicklas, Koichi Moriyama, Masayuki Numao:
Concept Drift Detection with Clustering via Statistical Change Detection Methods. KSE 2015: 37-42 - [c121]Pawel Matuszyk, João Vinagre, Myra Spiliopoulou, Alípio Mário Jorge, João Gama:
Forgetting methods for incremental matrix factorization in recommender systems. SAC 2015: 947-953 - [c120]Rui Sarmento, Mário Cordeiro, João Gama:
Visualization of evolving large scale ego-networks. SAC 2015: 960-962 - [c119]João Vinagre, Alípio Mário Jorge, João Gama:
Collaborative filtering with recency-based negative feedback. SAC 2015: 963-965 - [c118]Vinícius M. A. de Souza, Diego Furtado Silva, João Gama, Gustavo E. A. P. A. Batista:
Data Stream Classification Guided by Clustering on Nonstationary Environments and Extreme Verification Latency. SDM 2015: 873-881 - [e11]Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, Carlos Soares, João Gama, Alípio Jorge:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part I. Lecture Notes in Computer Science 9284, Springer 2015, ISBN 978-3-319-23527-1 [contents] - [e10]Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, João Gama, Alípio Jorge, Carlos Soares:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II. Lecture Notes in Computer Science 9285, Springer 2015, ISBN 978-3-319-23524-0 [contents] - [i5]João Vinagre, Alípio Mário Jorge, João Gama:
Evaluation of recommender systems in streaming environments. CoRR abs/1504.08175 (2015) - 2014
- [j49]João Gama, Indre Zliobaite, Albert Bifet, Mykola Pechenizkiy, Abdelhamid Bouchachia:
A survey on concept drift adaptation. ACM Comput. Surv. 46(4): 44:1-44:37 (2014) - [j48]Zoran Bosnic, Jaka Demsar, Grega Kespret, Pedro Pereira Rodrigues, João Gama, Igor Kononenko:
Enhancing data stream predictions with reliability estimators and explanation. Eng. Appl. Artif. Intell. 34: 178-192 (2014) - [j47]Rosane Maria Maffei Vallim, Jose Augusto Andrade Filho, Rodrigo Fernandes de Mello, André Carlos Ponce de Leon Ferreira de Carvalho, João Gama:
Unsupervised density-based behavior change detection in data streams. Intell. Data Anal. 18(2): 181-201 (2014) - [j46]João Gama, Petr Kosina:
Recurrent concepts in data streams classification. Knowl. Inf. Syst. 40(3): 489-507 (2014) - [j45]Hadi Fanaee-T, João Gama:
Event labeling combining ensemble detectors and background knowledge. Prog. Artif. Intell. 2(2-3): 113-127 (2014) - [j44]Raquel Sebastião, João Gama, Teresa Mendonça:
Constructing fading histograms from data streams. Prog. Artif. Intell. 3(1): 15-28 (2014) - [j43]Márcia D. B. Oliveira, Américo Guerreiro, João Gama:
Dynamic communities in evolving customer networks: an analysis using landmark and sliding windows. Soc. Netw. Anal. Min. 4(1): 208 (2014) - [j42]Pedro Pereira Rodrigues, João Gama:
Distributed clustering of ubiquitous data streams. WIREs Data Mining Knowl. Discov. 4(1): 38-54 (2014) - [j41]Mohamed Medhat Gaber, João Gama, Shonali Krishnaswamy, João Bártolo Gomes, Frederic T. Stahl:
Data stream mining in ubiquitous environments: state-of-the-art and current directions. WIREs Data Mining Knowl. Discov. 4(2): 116-138 (2014) - [c117]Anh Thu Vu, Gianmarco De Francisci Morales, João Gama, Albert Bifet:
Distributed Adaptive Model Rules for mining big data streams. IEEE BigData 2014: 345-353 - [c116]Cláudia Camila Dias, Cristina Granja, Altamiro da Costa Pereira, João Gama, Pedro Pereira Rodrigues:
Using Probabilistic Graphical Models to Enhance the Prognosis of Health-Related Quality of Life in Adult Survivors of Critical Illness. CBMS 2014: 56-61 - [c115]Pedro Mota Pereira, Rita P. Ribeiro, João Gama:
Failure Prediction - An Application in the Railway Industry. Discovery Science 2014: 264-275 - [c114]Raquel Sebastião, João Gama, Teresa Mendonça:
Comparing Data Distribution Using Fading Histograms. ECAI 2014: 1095-1096 - [c113]Vânia Almeida, João Gama:
Collaborative Wind Power Forecast. ICAIS 2014: 162-171 - [c112]João Gama:
Challenges in Learning from Streaming Data Extended Abstract. ICT Innovations 2014: 1-5 - [c111]Luís Moreira-Matias, João Gama, João Mendes-Moreira, Jorge Freire de Sousa:
An Incremental Probabilistic Model to Predict Bus Bunching in Real-Time. IDA 2014: 227-238 - [c110]João Gama:
Keynote speakers. ISCC 2014: 1 - [c109]Luís Moreira-Matias, Rafael Nunes, Michel Ferreira, João Mendes-Moreira, João Gama:
On Predicting a Call Center's Workload: A Discretization-Based Approach. ISMIS 2014: 548-553 - [c108]Luís Moreira-Matias, João Mendes-Moreira, Michel Ferreira, João Gama, Luís Damas:
An online learning framework for predicting the taxi stand's profitability. ITSC 2014: 2009-2014 - [c107]João Duarte, João Gama:
Ensembles of Adaptive Model Rules from High-Speed Data Streams. BigMine 2014: 198-213 - [c106]Rui Sarmento, Mário Cordeiro, João Gama:
Visualization for Streaming Telecommunications Networks. NFMCP 2014: 117-131 - [c105]João Vinagre, Alípio Mário Jorge, João Gama:
Fast Incremental Matrix Factorization for Recommendation with Positive-Only Feedback. UMAP 2014: 459-470 - [i4]Hadi Fanaee-T, João Gama:
An eigenvector-based hotspot detection. CoRR abs/1406.3191 (2014) - [i3]Hadi Fanaee-T, Márcia D. B. Oliveira, João Gama, Simon Malinowski, Ricardo Morla:
Event and Anomaly Detection Using Tucker3 Decomposition. CoRR abs/1406.3266 (2014) - [i2]Hadi Fanaee-T, João Gama:
EigenEvent: An Algorithm for Event Detection from Complex Data Streams in Syndromic Surveillance. CoRR abs/1406.3496 (2014) - [i1]Hadi Fanaee-T, João Gama:
Eigenspace Method for Spatiotemporal Hotspot Detection. CoRR abs/1406.3506 (2014) - 2013
- [j40]Jonathan de Andrade Silva, Elaine R. Faria, Rodrigo C. Barros, Eduardo R. Hruschka, André Carlos Ponce de Leon Ferreira de Carvalho, João Gama:
Data stream clustering: A survey. ACM Comput. Surv. 46(1): 13:1-13:31 (2013) - [j39]Márcia D. B. Oliveira, João Gama:
Visualization of evolving social networks using actor-level and community-level trajectories. Expert Syst. J. Knowl. Eng. 30(4): 306-319 (2013) - [j38]Raquel Sebastião, Margarida Martins da Silva, Rui Rabiço, João Gama, Teresa Mendonça:
Real-time algorithm for changes detection in depth of anesthesia signals. Evol. Syst. 4(1): 3-12 (2013) - [j37]João Gama:
Data Stream Mining: the Bounded Rationality. Informatica (Slovenia) 37(1): 21-25 (2013) - [j36]João Gama, Raquel Sebastião, Pedro Pereira Rodrigues:
On evaluating stream learning algorithms. Mach. Learn. 90(3): 317-346 (2013) - [j35]Luís Moreira-Matias, João Gama, Michel Ferreira, João Mendes-Moreira, Luís Damas:
Predicting Taxi-Passenger Demand Using Streaming Data. IEEE Trans. Intell. Transp. Syst. 14(3): 1393-1402 (2013) - [c104]Elaine R. Faria, Isabel J. C. R. Gonçalves, João Gama, André C. P. L. F. de Carvalho:
Evaluation Methodology for Multiclass Novelty Detection Algorithms. BRACIS 2013: 19-25 - [c103]Daniela Vasco, Pedro Pereira Rodrigues, João Gama:
Contextual anomalies in medical data. CBMS 2013: 544-545 - [c102]João Gama, Petr Kosina, Ezilda Almeida:
Avoiding Anomalies in Data Stream Learning. Discovery Science 2013: 49-63 - [c101]Luís Moreira-Matias, João Gama, Michel Ferreira, João Mendes-Moreira, Luís Damas:
On Predicting the Taxi-Passenger Demand: A Real-Time Approach. EPIA 2013: 54-65 - [c100]Douglas de O. Cardoso, João Gama, Massimo De Gregorio, Felipe M. G. França, Maurizio Giordano, Priscila M. V. Lima:
WIPS: the WiSARD Indoor Positioning System. ESANN 2013 - [c99]Ezilda Almeida, Carlos Abreu Ferreira, João Gama:
Learning Model Rules From High-Speed Data Streams. UDM@IJCAI 2013: 10 - [c98]Luís Moreira-Matias, Ricardo Fernandes, João Gama, Michel Ferreira, João Mendes-Moreira, Luís Damas:
On Recommending Urban Hotspots to Find Our Next Passenger. UDM@IJCAI 2013: 17 - [c97]Ezilda Almeida, Carlos Abreu Ferreira, João Gama:
Adaptive Model Rules from Data Streams. ECML/PKDD (1) 2013: 480-492 - [c96]Elaine R. Faria, João Gama, André C. P. L. F. de Carvalho:
Novelty detection algorithm for data streams multi-class problems. SAC 2013: 795-800 - [c95]Ezilda Almeida, Petr Kosina, João Gama:
Random rules from data streams. SAC 2013: 813-814 - [e9]Pedro Pereira Rodrigues, Mykola Pechenizkiy, João Gama, Ricardo Cruz-Correia, Jiming Liu, Agma J. M. Traina, Peter J. F. Lucas, Paolo Soda:
Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, Porto, Portugal, June 20-22, 2013. IEEE Computer Society 2013, ISBN 978-1-4799-1053-3 [contents] - [e8]João Gama, Michael May, Nuno Cavalheiro Marques, Paulo Cortez, Carlos Abreu Ferreira:
Proceedings of the 3rd Workshop on Ubiquitous Data Mining co-located with the 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013), Beijing, China, August 3, 2013. CEUR Workshop Proceedings 1088, CEUR-WS.org 2013 [contents] - 2012
- [j34]Márcia D. B. Oliveira, João Gama:
A framework to monitor clusters evolution applied to economy and finance problems. Intell. Data Anal. 16(1): 93-111 (2012) - [j33]João Gama:
A survey on learning from data streams: current and future trends. Prog. Artif. Intell. 1(1): 45-55 (2012) - [j32]Indre Zliobaite, Albert Bifet, Mohamed Medhat Gaber, Bogdan Gabrys, João Gama, Leandro L. Minku, Katarzyna Musial:
Next challenges for adaptive learning systems. SIGKDD Explor. 14(1): 48-55 (2012) - [j31]Márcia D. B. Oliveira, João Gama:
An overview of social network analysis. WIREs Data Mining Knowl. Discov. 2(2): 99-115 (2012) - [c94]Raquel Sebastião, Margarida Martins da Silva, João Gama, Teresa Mendonça:
Contributions to a decision support system based on depth of anesthesia signals. CBMS 2012: 1-6 - [c93]Carlos Abreu Ferreira, João Gama, Vítor Santos Costa, Vladimiro Miranda, Audun Botterud:
Predicting Ramp Events with a Stream-Based HMM Framework. Discovery Science 2012: 224-238 - [c92]Melissa Rodrigues, João Gama, Carlos Abreu Ferreira:
Identifying Relationships in Transactional Data. IBERAMIA 2012: 81-90 - [c91]Luís Moreira-Matias, João Gama, Michel Ferreira, João Mendes-Moreira, Luís Damas:
Online Predictive Model for Taxi Services. IDA 2012: 230-240 - [c90]Zaigham Faraz Siddiqui, Márcia D. B. Oliveira, João Gama, Myra Spiliopoulou:
Where Are We Going? Predicting the Evolution of Individuals. IDA 2012: 357-368 - [c89]