{"id":"https://openalex.org/W4391697820","doi":"https://doi.org/10.1109/icsai61474.2023.10423312","title":"Adaptive Ensemble with Density Peak and Random Forest for Class-Imbalance Learning","display_name":"Adaptive Ensemble with Density Peak and Random Forest for Class-Imbalance Learning","publication_year":2023,"publication_date":"2023-12-16","ids":{"openalex":"https://openalex.org/W4391697820","doi":"https://doi.org/10.1109/icsai61474.2023.10423312"},"language":"en","primary_location":{"id":"doi:10.1109/icsai61474.2023.10423312","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icsai61474.2023.10423312","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 9th International Conference on Systems and Informatics (ICSAI)","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/A5081900154","display_name":"Qiangkui Leng","orcid":"https://orcid.org/0000-0001-6788-3344"},"institutions":[{"id":"https://openalex.org/I176808543","display_name":"Liaoning Technical University","ror":"https://ror.org/01n2bd587","country_code":"CN","type":"education","lineage":["https://openalex.org/I176808543"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiangkui Leng","raw_affiliation_strings":["Liaoning Technical University,School of Electronics and Information Engineering,Huludao,China,125105"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Liaoning Technical University,School of Electronics and Information Engineering,Huludao,China,125105","institution_ids":["https://openalex.org/I176808543"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101784950","display_name":"Zeqi Zhang","orcid":"https://orcid.org/0000-0003-4051-8720"},"institutions":[{"id":"https://openalex.org/I176808543","display_name":"Liaoning Technical University","ror":"https://ror.org/01n2bd587","country_code":"CN","type":"education","lineage":["https://openalex.org/I176808543"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zeqi Zhang","raw_affiliation_strings":["Liaoning Technical University,School of Electronics and Information Engineering,Huludao,China,125105"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Liaoning Technical University,School of Electronics and Information Engineering,Huludao,China,125105","institution_ids":["https://openalex.org/I176808543"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033082094","display_name":"Xiangfu Meng","orcid":"https://orcid.org/0000-0003-0083-2117"},"institutions":[{"id":"https://openalex.org/I176808543","display_name":"Liaoning Technical University","ror":"https://ror.org/01n2bd587","country_code":"CN","type":"education","lineage":["https://openalex.org/I176808543"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangfu Meng","raw_affiliation_strings":["Liaoning Technical University,School of Electronics and Information Engineering,Huludao,China,125105"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Liaoning Technical University,School of Electronics and Information Engineering,Huludao,China,125105","institution_ids":["https://openalex.org/I176808543"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19952426,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"3","issue":null,"first_page":"1","last_page":"6"},"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":0.9965999722480774,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9872000217437744,"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/classifier","display_name":"Classifier (UML)","score":0.7356739044189453},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7134982943534851},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6883471012115479},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6409600377082825},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5775539875030518},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5765421986579895},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5362092852592468},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5187554359436035},{"id":"https://openalex.org/keywords/random-subspace-method","display_name":"Random subspace method","score":0.49767401814460754},{"id":"https://openalex.org/keywords/majority-rule","display_name":"Majority rule","score":0.43466469645500183},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4129686653614044},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36979055404663086}],"concepts":[{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7356739044189453},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7134982943534851},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6883471012115479},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6409600377082825},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5775539875030518},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5765421986579895},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5362092852592468},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5187554359436035},{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.49767401814460754},{"id":"https://openalex.org/C153668964","wikidata":"https://www.wikidata.org/wiki/Q27636","display_name":"Majority rule","level":2,"score":0.43466469645500183},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4129686653614044},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36979055404663086},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icsai61474.2023.10423312","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icsai61474.2023.10423312","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 9th International Conference on Systems and Informatics (ICSAI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","score":0.5799999833106995,"display_name":"Life in Land"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320323086","display_name":"Natural Science Foundation of Liaoning Province","ror":null},{"id":"https://openalex.org/F4320325567","display_name":"Liaoning Technical University","ror":"https://ror.org/01n2bd587"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1569512666","https://openalex.org/W2021732807","https://openalex.org/W2165835468","https://openalex.org/W2342049278","https://openalex.org/W2408920689","https://openalex.org/W2756359217","https://openalex.org/W2778854158","https://openalex.org/W2913668833","https://openalex.org/W3014150937","https://openalex.org/W3031232306","https://openalex.org/W3118096520","https://openalex.org/W4210741014","https://openalex.org/W4212883601","https://openalex.org/W4236137412","https://openalex.org/W4239510810","https://openalex.org/W6603460400","https://openalex.org/W6634326339"],"related_works":["https://openalex.org/W1981866886","https://openalex.org/W2889302474","https://openalex.org/W1964832275","https://openalex.org/W2389865566","https://openalex.org/W2052615004","https://openalex.org/W3080944905","https://openalex.org/W2913388591","https://openalex.org/W2905430408","https://openalex.org/W2375074607","https://openalex.org/W206413454"],"abstract_inverted_index":{"Classifying":[0],"imbalanced":[1,21,45,50],"data":[2],"poses":[3],"a":[4,17,73,115,155],"significant":[5],"challenge":[6],"in":[7,184,211],"machine":[8],"learning.":[9],"Employing":[10],"an":[11,32,140],"ensemble":[12,34],"strategy":[13],"stands":[14],"out":[15],"as":[16,80,86,100,109],"competitive":[18],"solution,":[19],"enhancing":[20],"classification":[22,46],"performance":[23,206],"through":[24],"multi-classifier":[25],"voting.":[26],"In":[27],"this":[28],"study,":[29],"we":[30],"introduce":[31],"adaptive":[33],"method,":[35],"leveraging":[36],"density":[37,56],"peak":[38,57],"clustering":[39],"and":[40,63,84,105,172,215],"random":[41],"forest,":[42],"to":[43,120,153,179],"tackle":[44],"issues.":[47],"Initially,":[48],"the":[49,61,81,87,97,101,106,110,122,132,135,148,160,185],"dataset's":[51],"majority":[52,68,142],"class":[53,69,143,150],"samples":[54],"undergo":[55],"clustering.":[58],"We":[59],"calculate":[60],"\u03c1":[62,79],"\u03b4":[64,85],"values":[65],"for":[66],"each":[67],"sample":[70,103,112,144,151],"point,":[71],"establishing":[72],"sequence":[74,91],"by":[75],"descending":[76],"order":[77,83],"of":[78,124,134,213],"primary":[82],"secondary":[88],"order.":[89],"This":[90,138],"is":[92,118,177,191],"then":[93],"equitably":[94],"split,":[95],"identifying":[96],"first":[98],"half":[99,108],"core":[102],"set":[104,152],"second":[107],"boundary":[111,125],"set.":[113],"Subsequently,":[114],"dynamic":[116],"function":[117],"employed":[119,178],"control":[121],"weight":[123,129],"samples,":[126],"adjusting":[127],"sampling":[128],"based":[130],"on":[131,207],"iterations":[133],"integrated":[136],"classifier.":[137,187],"yields":[139],"undersampled":[141],"set,":[145],"combined":[146],"with":[147,193],"minority":[149],"form":[154],"new":[156],"training":[157,165],"subset.":[158],"Finally,":[159],"classifier":[161],"learns":[162],"from":[163],"diverse":[164],"subsets,":[166],"generating":[167],"subclassifiers":[168],"that":[169],"exhibit":[170],"balance":[171],"variability.":[173],"A":[174],"voting":[175],"mechanism":[176],"integrate":[180],"all":[181],"subclassifiers,":[182],"resulting":[183],"final":[186],"Our":[188],"proposed":[189],"method":[190],"compared":[192],"eight":[194],"established":[195],"approaches":[196],"across":[197],"twelve":[198],"benchmark":[199],"datasets.":[200],"The":[201],"results":[202],"demonstrate":[203],"its":[204],"superior":[205],"most":[208],"datasets,":[209],"particularly":[210],"terms":[212],"F1":[214],"Gmean.":[216]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
