{"id":"https://openalex.org/W2070135163","doi":"https://doi.org/10.1109/ijcnn.2014.6889667","title":"Learning from combination of data chunks for multi-class imbalanced data","display_name":"Learning from combination of data chunks for multi-class imbalanced data","publication_year":2014,"publication_date":"2014-07-01","ids":{"openalex":"https://openalex.org/W2070135163","doi":"https://doi.org/10.1109/ijcnn.2014.6889667","mag":"2070135163"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2014.6889667","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2014.6889667","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 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/A5060673071","display_name":"Xuying Liu","orcid":"https://orcid.org/0000-0001-9190-4651"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xu-Ying Liu","raw_affiliation_strings":["National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100383897","display_name":"Qianqian Li","orcid":"https://orcid.org/0000-0002-3866-8159"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian-Qian Li","raw_affiliation_strings":["MOE, Southeast University, Naniing, China"],"affiliations":[{"raw_affiliation_string":"MOE, Southeast University, Naniing, China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5060673071"],"corresponding_institution_ids":["https://openalex.org/I881766915"],"apc_list":null,"apc_paid":null,"fwci":0.818,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.80970003,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"14","issue":null,"first_page":"1680","last_page":"1687"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9997000098228455,"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":0.9997000098228455,"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/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9488000273704529,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.7633464336395264},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6883724927902222},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5625903010368347},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.5475524067878723},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.49916601181030273},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.48657405376434326},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4727776050567627},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45802435278892517},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42873668670654297},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.4249206483364105},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4217432141304016},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35026901960372925},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2070198655128479},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.1416599452495575}],"concepts":[{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.7633464336395264},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6883724927902222},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5625903010368347},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.5475524067878723},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.49916601181030273},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.48657405376434326},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4727776050567627},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45802435278892517},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42873668670654297},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.4249206483364105},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4217432141304016},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35026901960372925},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2070198655128479},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.1416599452495575},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2014.6889667","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2014.6889667","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W68536248","https://openalex.org/W85350352","https://openalex.org/W145450961","https://openalex.org/W1563938718","https://openalex.org/W1580049798","https://openalex.org/W1594031697","https://openalex.org/W1660390307","https://openalex.org/W1912982817","https://openalex.org/W1941659294","https://openalex.org/W1965895350","https://openalex.org/W1973798076","https://openalex.org/W1988790447","https://openalex.org/W1994399046","https://openalex.org/W2018186689","https://openalex.org/W2024223694","https://openalex.org/W2070808135","https://openalex.org/W2076118331","https://openalex.org/W2083551746","https://openalex.org/W2096945460","https://openalex.org/W2100128988","https://openalex.org/W2104167780","https://openalex.org/W2104577112","https://openalex.org/W2104955141","https://openalex.org/W2106030336","https://openalex.org/W2108949035","https://openalex.org/W2116825089","https://openalex.org/W2118978333","https://openalex.org/W2119191234","https://openalex.org/W2131118883","https://openalex.org/W2135493362","https://openalex.org/W2148143831","https://openalex.org/W2152761983","https://openalex.org/W2155653793","https://openalex.org/W2157526632","https://openalex.org/W2158059015","https://openalex.org/W2158896888","https://openalex.org/W2164341120","https://openalex.org/W2369342869","https://openalex.org/W2538183362","https://openalex.org/W2979832949","https://openalex.org/W4212883601","https://openalex.org/W4232478844","https://openalex.org/W4248437541","https://openalex.org/W6603460400","https://openalex.org/W6605907934","https://openalex.org/W6633571703","https://openalex.org/W6634569365","https://openalex.org/W6675785006","https://openalex.org/W6676146333","https://openalex.org/W6680391020","https://openalex.org/W7027963998"],"related_works":["https://openalex.org/W4255837520","https://openalex.org/W2387011115","https://openalex.org/W4234808182","https://openalex.org/W2382043075","https://openalex.org/W2809151339","https://openalex.org/W2216913934","https://openalex.org/W2360673138","https://openalex.org/W2809370583","https://openalex.org/W4301447905","https://openalex.org/W2333722679"],"abstract_inverted_index":{"Class-imbalance":[0],"is":[1,18,25,38,172,209,220,232],"very":[2],"common":[3],"in":[4,71,215,227],"real-world":[5],"applications.":[6],"Previous":[7],"studies":[8],"focused":[9],"on":[10],"binary-class":[11,32,228],"imbalance":[12,16],"problem,":[13],"whereas":[14],"multi-class":[15,41,180,204,212],"problem":[17],"more":[19,22,45],"general":[20],"and":[21,28,58,138,187,200,230],"challenging.":[23],"Under-sampling":[24],"an":[26],"effective":[27],"efficient":[29],"method":[30,83],"for":[31,40,179,211],"imbalanced":[33,42,181,213],"data.":[34,65],"But":[35],"when":[36,183],"it":[37,89,171,219,231],"used":[39,190],"data,":[43],"many":[44,175,216],"majority":[46,56,73,87,144],"class":[47,61,74,126,137,145],"examples":[48,75],"are":[49,53,189],"ignored":[50,76],"because":[51,218],"there":[52],"often":[54,62],"multiple":[55,92],"classes,":[57],"the":[59,68,72,103,111,120,124,135,158,165,224],"minority":[60,125,136],"has":[63,119],"few":[64],"To":[66],"utilize":[67],"information":[69,105],"contained":[70],"by":[77],"under-sampling,":[78],"this":[79],"paper":[80],"proposes":[81],"a":[82,107,147,203],"ChunkCombine.":[84],"For":[85],"each":[86,139],"class,":[88],"performs":[90],"under-sampling":[91],"times":[93],"to":[94,127,155,234],"obtained":[95],"non-overlapping":[96],"data":[97,108,117,140,182,214],"chunks,":[98],"such":[99],"that":[100,106],"they":[101],"contain":[102],"most":[104],"sample":[109],"of":[110,134],"same":[112,121],"size":[113,122],"can":[114],"contain.":[115],"Each":[116],"chunk":[118,141],"as":[123,191],"achieve":[128],"balance.":[129],"Then":[130],"every":[131,143],"possible":[132,166],"combination":[133],"from":[142,157,163],"forms":[146],"balanced":[148],"training":[149,160],"set.":[150],"ChunkCombine":[151],"uses":[152],"ensemble":[153],"techniques":[154],"learn":[156],"different":[159,197],"sets":[161],"derived":[162],"all":[164],"combinations.":[167],"Experimental":[168],"results":[169],"show":[170],"better":[173],"than":[174],"other":[176],"popular":[177],"methods":[178],"average":[184],"accuracy,":[185],"G-mean":[186],"MAUC":[188],"evaluation":[192,198],"measures.":[193],"Besides,":[194],"we":[195],"discuss":[196],"measures":[199],"suggest":[201],"that,":[202],"F-measure":[205,226],"Mean":[206],"F-Measure":[207],"(MFM)":[208],"unsuitable":[210],"situations":[217],"not":[221],"consistent":[222],"with":[223],"standard":[225],"case":[229],"close":[233],"accuracy.":[235]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}