{"id":"https://openalex.org/W7626864","doi":null,"title":"Mixture of Expert Agents for Handling Imbalanced Data Sets","display_name":"Mixture of Expert Agents for Handling Imbalanced Data Sets","publication_year":2003,"publication_date":"2003-01-01","ids":{"openalex":"https://openalex.org/W7626864","mag":"7626864"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.58.3247","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.58.3247","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://journals.teilar.gr/amct/pub/2003/4_Pintelas.pdf","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5066370772","display_name":"Sotiris B. Kotsiantis","orcid":"https://orcid.org/0000-0002-2247-3082","observed_orcids":["https://orcid.org/0000-0002-2247-3082"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"S. B. Kotsiantis","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5024258131","display_name":"Panagiotis Pintelas","orcid":"https://orcid.org/0000-0001-8436-2743","observed_orcids":["https://orcid.org/0000-0001-8436-2743"]},"institutions":[{"id":"https://openalex.org/I174878644","display_name":"University of Patras","ror":"https://ror.org/017wvtq80","country_code":"GR","type":"education","lineage":["https://openalex.org/I174878644"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"P. E. Pintelas","raw_affiliation_strings":["university of patras"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"university of patras","institution_ids":["https://openalex.org/I174878644"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0407,"has_fulltext":false,"cited_by_count":81,"citation_normalized_percentile":{"value":0.78567942,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"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":0.9994999766349792,"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":0.9994999766349792,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.0003000000142492354,"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/T12761","display_name":"Data Stream Mining Techniques","score":9.999999747378752e-05,"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/class-imbalance","display_name":"class imbalance","score":0.9279999732971191},{"id":"https://openalex.org/keywords/minority-class","display_name":"minority class","score":0.753000020980835},{"id":"https://openalex.org/keywords/ensemble-classifiers","display_name":"ensemble classifiers","score":0.6890000104904175},{"id":"https://openalex.org/keywords/predictive-analytics","display_name":"predictive analytics","score":0.5509999990463257}],"concepts":[{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.6350080370903015},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6086265444755554},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5831964015960693},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5443112850189209},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.502556324005127},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4901655614376068},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.4886839687824249}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.58.3247","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.58.3247","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://journals.teilar.gr/amct/pub/2003/4_Pintelas.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.59.2997","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.59.2997","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://journals.teilar.gr/amct/pub/2003/4_Pintelas.ps","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.4000000059604645}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W14420165","https://openalex.org/W85350352","https://openalex.org/W142793689","https://openalex.org/W1517113043","https://openalex.org/W1549887922","https://openalex.org/W1580049798","https://openalex.org/W1588282782","https://openalex.org/W1780185704","https://openalex.org/W1941659294","https://openalex.org/W2040424159","https://openalex.org/W2058732827","https://openalex.org/W2084812512","https://openalex.org/W2096942889","https://openalex.org/W2105779241","https://openalex.org/W2114350817","https://openalex.org/W2123504579","https://openalex.org/W2125055259","https://openalex.org/W2140785063","https://openalex.org/W2148143831","https://openalex.org/W2160150610","https://openalex.org/W2161349318","https://openalex.org/W2911678770"],"related_works":["https://openalex.org/W2026877895","https://openalex.org/W7098386352","https://openalex.org/W1797364528","https://openalex.org/W7152452867","https://openalex.org/W4412049267","https://openalex.org/W2103768134","https://openalex.org/W2155059851","https://openalex.org/W77359230","https://openalex.org/W2616891418","https://openalex.org/W2091007025"],"abstract_inverted_index":{"Many":[0],"real-world":[1],"data":[2,35],"sets":[3],"exhibit":[4],"skewed":[5],"class":[6,17,46],"distributions":[7],"in":[8,117,129],"which":[9],"almost":[10],"all":[11],"cases":[12,21],"are":[13],"allotted":[14],"to":[15,22,70,103,109],"a":[16,23,39,60,84,95,99],"and":[18,47,90],"far":[19],"fewer":[20],"smaller,":[24],"usually":[25],"more":[26,100],"interesting":[27],"class.":[28,55],"A":[29],"classifier":[30],"induced":[31],"from":[32],"an":[33,48,77,130],"imbalanced":[34],"set":[36],"has,":[37],"typically,":[38],"low":[40],"error":[41,50],"rate":[42,51],"for":[43,52],"the":[44,53,64,104,122,126],"majority":[45],"unacceptable":[49],"minority":[54],"This":[56],"paper":[57],"firstly":[58],"provides":[59],"systematic":[61],"study":[62,79],"on":[63],"various":[65],"methodologies":[66,82],"that":[67,93],"have":[68],"tried":[69],"handle":[71],"this":[72],"problem.":[73,105],"Finally,":[74],"it":[75,91],"presents":[76],"experimental":[78],"of":[80,87,113,125],"these":[81],"with":[83],"proposed":[85],"mixture":[86],"expert":[88],"agents":[89],"concludes":[92],"such":[94],"framework":[96],"can":[97],"be":[98],"effective":[101],"solution":[102],"Our":[106],"method":[107],"seems":[108],"allow":[110],"improved":[111],"identification":[112],"difficult":[114],"small":[115],"classes":[116,128],"predictive":[118],"analysis,":[119],"while":[120],"keeping":[121],"classification":[123],"ability":[124],"other":[127],"acceptable":[131],"level.":[132]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":6},{"year":2014,"cited_by_count":6},{"year":2013,"cited_by_count":5},{"year":2012,"cited_by_count":7}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2025-10-27T00:00:00"}
