iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://api.openalex.org/works/doi:10.1007/11867586_2
{"id":"https://openalex.org/W1571478088","doi":"https://doi.org/10.1007/11867586_2","title":"On Optimizing Dissimilarity-Based Classification Using Prototype Reduction Schemes","display_name":"On Optimizing Dissimilarity-Based Classification Using Prototype Reduction Schemes","publication_year":2006,"publication_date":"2006-01-01","ids":{"openalex":"https://openalex.org/W1571478088","doi":"https://doi.org/10.1007/11867586_2","mag":"1571478088"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1007/11867586_2","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319965","https://openalex.org/P4310319900"],"host_organization_lineage_names":["Springer Nature","Springer Science+Business Media"],"type":"book series"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"book-chapter","type_crossref":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114858718","display_name":"Sang\u2010Woon Kim","orcid":"https://orcid.org/0000-0002-3172-8462"},"institutions":[{"id":"https://openalex.org/I89440247","display_name":"Myongji University","ror":"https://ror.org/00s9dpb54","country_code":"KR","type":"education","lineage":["https://openalex.org/I89440247"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sang-Woon Kim","raw_affiliation_strings":["Department of Computer Science and Engineering, Myongji University, Yongin, Korea."],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Myongji University, Yongin, Korea.","institution_ids":["https://openalex.org/I89440247"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055634885","display_name":"B. John Oommen","orcid":"https://orcid.org/0000-0002-5105-1575"},"institutions":[{"id":"https://openalex.org/I67031392","display_name":"Carleton University","ror":"https://ror.org/02qtvee93","country_code":"CA","type":"education","lineage":["https://openalex.org/I67031392"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"B. John Oommen","raw_affiliation_strings":["#N# \u2020 School of Computer Science, Carleton University, Ottawa, Canada"],"affiliations":[{"raw_affiliation_string":"#N# \u2020 School of Computer Science, Carleton University, Ottawa, Canada","institution_ids":["https://openalex.org/I67031392"]}]}],"institution_assertions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":5000,"currency":"EUR","value_usd":5392,"provenance":"doaj"},"apc_paid":null,"fwci":1.115,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.539176,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":76,"max":78},"biblio":{"volume":null,"issue":null,"first_page":"15","last_page":"28"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Learning with Noisy Labels in Machine Learning","score":0.9997,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12535","display_name":"Learning with Noisy Labels in Machine Learning","score":0.9997,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face Recognition and Dimensionality Reduction Techniques","score":0.9984,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11975","display_name":"Application of Genetic Programming in Machine Learning","score":0.9963,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/mahalanobis-distance","display_name":"Mahalanobis distance","score":0.81089056},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality Reduction","score":0.594208},{"id":"https://openalex.org/keywords/robust-learning","display_name":"Robust Learning","score":0.548523},{"id":"https://openalex.org/keywords/learning-classifier-systems","display_name":"Learning Classifier Systems","score":0.543581},{"id":"https://openalex.org/keywords/dbc","display_name":"dBc","score":0.52362067},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"Hyperparameter Optimization","score":0.510329},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral Clustering","score":0.510048},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4331938}],"concepts":[{"id":"https://openalex.org/C1921717","wikidata":"https://www.wikidata.org/wiki/Q1334846","display_name":"Mahalanobis distance","level":2,"score":0.81089056},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.80503154},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.65898263},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.63259095},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.62868595},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.59675765},{"id":"https://openalex.org/C193523891","wikidata":"https://www.wikidata.org/wiki/Q1771950","display_name":"dBc","level":3,"score":0.52362067},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.49704102},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.46597004},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4331938},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4208583},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38566655},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.24239823},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1007/11867586_2","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319965","https://openalex.org/P4310319900"],"host_organization_lineage_names":["Springer Nature","Springer Science+Business Media"],"type":"book series"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":24,"referenced_works":["https://openalex.org/W1610836425","https://openalex.org/W1679913846","https://openalex.org/W1770825568","https://openalex.org/W1967550647","https://openalex.org/W1982606067","https://openalex.org/W1994410331","https://openalex.org/W2019762723","https://openalex.org/W2056609019","https://openalex.org/W2069473723","https://openalex.org/W2092353981","https://openalex.org/W2104968725","https://openalex.org/W2116283693","https://openalex.org/W2124735751","https://openalex.org/W2132549764","https://openalex.org/W2135346934","https://openalex.org/W2135807716","https://openalex.org/W2138722234","https://openalex.org/W2138798794","https://openalex.org/W2139212933","https://openalex.org/W2150563191","https://openalex.org/W2900216653","https://openalex.org/W3193477162","https://openalex.org/W4213332169","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W939486154","https://openalex.org/W4382795578","https://openalex.org/W2778199868","https://openalex.org/W2771741613","https://openalex.org/W2420560403","https://openalex.org/W2402648945","https://openalex.org/W2355463328","https://openalex.org/W2157426608","https://openalex.org/W1491333300","https://openalex.org/W1431147547"],"abstract_inverted_index":{"The":[0,73],"aim":[1],"of":[2,46,60,99,131,170,180],"this":[3,76,117,165],"paper":[4,203],"is":[5,43,53,107],"to":[6,21,82,113,124,191,216],"present":[7],"a":[8,12,44,67,121,157,181],"strategy":[9,77,123],"by":[10,32,194],"which":[11],"new":[13],"philosophy":[14],"for":[15,90,128,240],"pattern":[16],"classification,":[17],"namely":[18],"that":[19,149,226],"pertaining":[20],"Dissimilarity-Based":[22],"Classifiers":[23],"(DBCs),":[24],"can":[25],"be":[26,153,192],"efficiently":[27],"implemented.":[28],"This":[29],"methodology,":[30],"proposed":[31,228],"Duin":[33],"and":[34,52,85,95,137],"his":[35],"co-authors":[36],"(see":[37],"[3],":[38],"[4],":[39],"[5],":[40],"[6],":[41],"[8]),":[42],"way":[45],"defining":[47],"classifiers":[48],"between":[49,71],"the":[50,57,61,80,87,92,97,100,104,110,126,139,143,150,168,178,189,195,202,209,213,218,227,231,237],"classes,":[51],"not":[54],"based":[55,141],"on":[56,66,109,142,167],"feature":[58],"measurements":[59],"individual":[62],"patterns,":[63],"but":[64],"rather":[65,162],"suitable":[68],"dissimilarity":[69,105],"measure":[70],"them.":[72],"problem":[74],"with":[75,236],"is,":[78],"however,":[79],"need":[81],"compute,":[83,135],"store":[84,136],"process":[86,138],"inter-pattern":[88],"dissimilarities":[89],"all":[91,129],"training":[93,151],"samples,":[94],"thus,":[96],"accuracy":[98,233],"classifier":[101],"designed":[102],"in":[103,201],"space":[106],"dependent":[108],"methods":[111],"used":[112],"achieve":[114],"this.":[115],"In":[116],"paper,":[118],"we":[119,147,176,204],"suggest":[120],"novel":[122],"enhance":[125],"computation":[127],"families":[130],"DBCs.":[132],"Rather":[133],"than":[134,163],"DBC":[140],"entire":[144],"data":[145,248],"set,":[146],"advocate":[148,177],"set":[152],"first":[154],"reduced":[155],"into":[156],"smaller":[158],"representative":[159],"subset.":[160],"Also,":[161],"determine":[164],"subset":[166],"basis":[169],"random":[171],"selection,":[172],"or":[173],"clustering":[174],"etc.,":[175],"use":[179],"Prototype":[182],"Reduction":[183],"Scheme":[184],"(PRS),":[185],"whose":[186],"output":[187],"yields":[188],"points":[190],"utilized":[193],"DBC.":[196],"Apart":[197],"from":[198],"utilizing":[199],"PRSs,":[200],"also":[205],"propose":[206],"simultaneously":[207],"employing":[208],"Mahalanobis":[210],"distance":[211],"as":[212,244,246],"dissimilarity-measurement":[214],"criterion":[215],"increase":[217],"DBC\u2019s":[219],"classification":[220,232],"accuracy.":[221],"Our":[222],"experimental":[223],"results":[224],"demonstrate":[225],"mechanism":[229],"increases":[230],"when":[234],"compared":[235],"\u201cconventional\u201d":[238],"approaches":[239],"samples":[241],"involving":[242],"real-life":[243],"well":[245],"artificial":[247],"sets.":[249]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W1571478088","counts_by_year":[{"year":2014,"cited_by_count":1}],"updated_date":"2024-11-30T15:56:15.951056","created_date":"2016-06-24"}