Cost-Sensitive Learning / ReSampling / Weighting / Thresholding / BorderlineSMOTE / AdaCost / etc.

Theano implementation of Cost-Sensitive Deep Neural Networks

This repo contains implementation of advanced ML techniques. Includes model ensembles, cost-sensitive learning and dealing with class imbalance.

Pytorch implementation for paper 'BANNER: A Cost-Sensitive Contextualized Model for Bangla Named Entity Recognition'

Advanced Machine Learning Algorithms including Cost-Sensitive Learning, Class Imbalances, Multi-Label Data, Multi-Instance Learning, Active Learning, Multi-Relational Data Mining, Interpretability in Python using Scikit-Learn.

A python implementation of a genetic algorithm based approach for cost sensitive learning

A genetic algorithm based approach for cost sensitive learning, in which the misclassification cost is considered together with the cost of feature extraction.

Deep Cost-sensitive Kernel Machine Model - PAKDD 2020

Implementation of cost sensitive KNN algorithm described in Qin, et al, 2013

Predicting whether an African country will be in recession or not with advanced machine learning techniques involving class imbalance, cost-sensitive learning and explainable machine learning

Solution to the Data Mining Cup 2019 competition

Official code for our paper - "Melanoma classification from dermatoscopy images using knowledge distillation for highly imbalanced data".

A cost-sensitive BERT that handles the class imbalance for the task of biomedical NER.

This work focuses on the development of machine learning models, in particular neural networks and SVM, where they can detect toxicity in comments. The topics we will be dealing with: a) Cost-sensitive learning, b) Class imbalance

Worked on detecting illicit transactions in the Ethereum Transactions dataset by increasing our dataset size, and with little tolerance to missing fraudulent transactions.

Paper under review on "Multimedia Tools and Applications" journal.

Supplementary codes of the Master Thesis "Binary Classification on Imbalanced Datasets"

R package for dealing with cost-sensitive learning (class imbalance and classification error cost) in a multiclass setting using lasso regularized logistic regression and gradient boosted decision trees.

Software to build Decision Trees for imbalanced data. To cite this Original Software Publication: https://www.sciencedirect.com/science/article/pii/S2352711021001242

Cost Sensitive Learning in German Credit Data