{"id":"https://openalex.org/W2550538833","doi":"https://doi.org/10.1109/ijcnn.2016.7727503","title":"Cost-sensitive one-vs-one ensemble for multi-class imbalanced data","display_name":"Cost-sensitive one-vs-one ensemble for multi-class imbalanced data","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2550538833","doi":"https://doi.org/10.1109/ijcnn.2016.7727503","mag":"2550538833"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2016.7727503","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727503","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","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/A5054879396","display_name":"Bartosz Krawczyk","orcid":"https://orcid.org/0000-0002-9774-0106"},"institutions":[{"id":"https://openalex.org/I11923345","display_name":"Wroc\u0142aw University of Science and Technology","ror":"https://ror.org/008fyn775","country_code":"PL","type":"education","lineage":["https://openalex.org/I11923345"]}],"countries":["PL"],"is_corresponding":true,"raw_author_name":"Bartosz Krawczyk","raw_affiliation_strings":["Department of Systems and Computer Networks, Wroclaw University of Technology, Wroclaw, Poland"],"affiliations":[{"raw_affiliation_string":"Department of Systems and Computer Networks, Wroclaw University of Technology, Wroclaw, Poland","institution_ids":["https://openalex.org/I11923345"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5054879396"],"corresponding_institution_ids":["https://openalex.org/I11923345"],"apc_list":null,"apc_paid":null,"fwci":5.1417,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.95834775,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9998999834060669,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9998999834060669,"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/T13429","display_name":"Electricity Theft Detection Techniques","score":0.9912999868392944,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9883000254631042,"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/computer-science","display_name":"Computer science","score":0.7303290963172913},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7051356434822083},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6917945146560669},{"id":"https://openalex.org/keywords/divide-and-conquer-algorithms","display_name":"Divide and conquer algorithms","score":0.5384236574172974},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.522952139377594},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.47322264313697815},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43907430768013},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.42786988615989685},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.41387617588043213},{"id":"https://openalex.org/keywords/one-class-classification","display_name":"One-class classification","score":0.41252484917640686},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36719250679016113},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.27916744351387024},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2586263418197632},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16744789481163025}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7303290963172913},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7051356434822083},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6917945146560669},{"id":"https://openalex.org/C71559656","wikidata":"https://www.wikidata.org/wiki/Q671298","display_name":"Divide and conquer algorithms","level":2,"score":0.5384236574172974},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.522952139377594},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.47322264313697815},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43907430768013},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.42786988615989685},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.41387617588043213},{"id":"https://openalex.org/C34872919","wikidata":"https://www.wikidata.org/wiki/Q7092302","display_name":"One-class classification","level":3,"score":0.41252484917640686},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36719250679016113},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.27916744351387024},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2586263418197632},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16744789481163025},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2016.7727503","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727503","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W136027474","https://openalex.org/W142256263","https://openalex.org/W1481178697","https://openalex.org/W1533970595","https://openalex.org/W1541819481","https://openalex.org/W1975314995","https://openalex.org/W1984323748","https://openalex.org/W2000766290","https://openalex.org/W2013238257","https://openalex.org/W2015452969","https://openalex.org/W2024223694","https://openalex.org/W2038705219","https://openalex.org/W2063598178","https://openalex.org/W2081183038","https://openalex.org/W2088059023","https://openalex.org/W2099454382","https://openalex.org/W2100208452","https://openalex.org/W2119191234","https://openalex.org/W2123977051","https://openalex.org/W2124710650","https://openalex.org/W2136256517","https://openalex.org/W2142692934","https://openalex.org/W2143017915","https://openalex.org/W2143908786","https://openalex.org/W2146194630","https://openalex.org/W2164341120","https://openalex.org/W2294539135","https://openalex.org/W6631885290","https://openalex.org/W6678797503","https://openalex.org/W6680873320"],"related_works":["https://openalex.org/W4376528628","https://openalex.org/W2046798493","https://openalex.org/W4382934300","https://openalex.org/W2121061354","https://openalex.org/W4285388059","https://openalex.org/W1997162386","https://openalex.org/W2560147515","https://openalex.org/W2510206144","https://openalex.org/W4388963631","https://openalex.org/W2165299150"],"abstract_inverted_index":{"Learning":[0],"from":[1],"imbalanced":[2],"data":[3,125],"poses":[4],"significant":[5],"challenges":[6],"for":[7,39,90,95,105,178,200],"machine":[8],"learning":[9],"algorithms,":[10],"as":[11,76],"they":[12],"need":[13,38],"to":[14,81,118,232],"deal":[15],"with":[16,114,145,157,221],"uneven":[17],"distribution":[18],"of":[19,49,129,154],"examples":[20],"in":[21,240],"the":[22,32,111,152,155,170,181,185,197,214,241],"training":[23],"set.":[24],"As":[25],"standard":[26],"classifiers":[27],"will":[28],"be":[29,61,86],"biased":[30],"towards":[31,169],"majority":[33,56,94],"class":[34,59,84,89,202],"there":[35],"exist":[36],"a":[37,64,87,93,127,141,158,175,207],"specific":[40],"methods":[41,238],"than":[42],"can":[43,60,85,195],"overcome":[44],"this":[45,98],"single-class":[46],"dominance.":[47],"Most":[48],"works":[50],"concentrated":[51],"on":[52,150,184],"binary":[53,130],"problems,":[54],"where":[55],"and":[57,236],"minority":[58,88,171],"distinguished.":[62],"But":[63],"more":[65],"challenging":[66],"problem":[67,112],"arises":[68],"when":[69],"imbalance":[70],"is":[71,230],"present":[72],"within":[73],"multi-class":[74,124,216],"datasets,":[75],"relations":[77],"between":[78],"classes":[79],"tend":[80],"complicate.":[82],"One":[83],"some,":[91],"while":[92],"others.":[96],"In":[97],"paper,":[99],"we":[100,121,139,164,194,212],"propose":[101,174],"an":[102,228],"efficient":[103],"method":[104,177],"handling":[106],"such":[107,227],"scenarios,":[108],"that":[109,226],"combines":[110],"decomposition":[113,237],"cost-sensitive":[115,142],"learning.":[116],"According":[117],"divide-and-conquer":[119],"rule,":[120],"decompose":[122],"our":[123,166],"into":[126],"number":[128],"subproblems":[131],"using":[132,206],"one-versus-one":[133],"approach.":[134],"To":[135],"each":[136,201],"simplified":[137],"task":[138],"delegate":[140],"neural":[143],"network":[144],"moving":[146],"threshold.":[147],"It":[148],"relies":[149],"scaling":[151],"output":[153],"classifier":[156,209],"given":[159],"cost":[160,198],"function.":[161],"This":[162,192],"way,":[163],"adjust":[165],"support":[167],"functions":[168],"class.":[172],"We":[173],"novel":[176],"automatically":[179],"determining":[180],"cost,":[182],"based":[183],"Receiver":[186],"Operating":[187],"Characteristic":[188],"(ROC)":[189],"curve":[190],"analysis.":[191],"way":[193],"estimate":[196],"matrix":[199],"pair":[203],"independently.":[204],"Then":[205],"dedicated":[208],"fusion":[210],"approach,":[211],"reconstruct":[213],"original":[215],"problem.":[217],"Experimental":[218],"analysis":[219],"backed-up":[220],"statistical":[222],"testing":[223],"clearly":[224],"proves":[225],"approach":[229],"superior":[231],"state-of-the":[233],"art":[234],"ad-hoc":[235],"used":[239],"literature.":[242]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}