{"id":"https://openalex.org/W2099279324","doi":"https://doi.org/10.1109/cbms.2009.5255382","title":"An experimental comparison of MES aggregation rules in case of imbalanced datasets","display_name":"An experimental comparison of MES aggregation rules in case of imbalanced datasets","publication_year":2009,"publication_date":"2009-08-01","ids":{"openalex":"https://openalex.org/W2099279324","doi":"https://doi.org/10.1109/cbms.2009.5255382","mag":"2099279324"},"language":"en","primary_location":{"id":"doi:10.1109/cbms.2009.5255382","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cbms.2009.5255382","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 22nd IEEE International Symposium on Computer-Based Medical Systems","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/A5003216983","display_name":"Paolo Soda","orcid":"https://orcid.org/0000-0003-2621-072X"},"institutions":[{"id":"https://openalex.org/I155125353","display_name":"Universit\u00e0 Campus Bio-Medico","ror":"https://ror.org/04gqx4x78","country_code":"IT","type":"education","lineage":["https://openalex.org/I155125353"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Paolo Soda","raw_affiliation_strings":["Integrated Research Centre, Universita Campus Bio Medico Di Roma, Rome, Italy","University Campus Bio-Medico of Rome, Integrated Research Centre, Rome, Italy"],"affiliations":[{"raw_affiliation_string":"Integrated Research Centre, Universita Campus Bio Medico Di Roma, Rome, Italy","institution_ids":["https://openalex.org/I155125353"]},{"raw_affiliation_string":"University Campus Bio-Medico of Rome, Integrated Research Centre, Rome, Italy","institution_ids":["https://openalex.org/I155125353"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5003216983"],"corresponding_institution_ids":["https://openalex.org/I155125353"],"apc_list":null,"apc_paid":null,"fwci":1.3606,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.84931332,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.9957000017166138,"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":0.719483494758606},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7171728610992432},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.625016450881958},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6168898344039917},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5837510228157043},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5591666102409363},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.466691792011261},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.45271313190460205},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4350912570953369},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32461684942245483}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.719483494758606},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7171728610992432},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.625016450881958},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6168898344039917},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5837510228157043},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5591666102409363},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.466691792011261},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.45271313190460205},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4350912570953369},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32461684942245483},{"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/cbms.2009.5255382","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cbms.2009.5255382","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 22nd IEEE International Symposium on Computer-Based Medical Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.46000000834465027}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W7626864","https://openalex.org/W85350352","https://openalex.org/W1598876617","https://openalex.org/W1824259148","https://openalex.org/W1850501806","https://openalex.org/W1934727243","https://openalex.org/W1986515506","https://openalex.org/W1991464299","https://openalex.org/W1993220166","https://openalex.org/W2011376672","https://openalex.org/W2016648380","https://openalex.org/W2058732827","https://openalex.org/W2098154993","https://openalex.org/W2100208452","https://openalex.org/W2114968414","https://openalex.org/W2134546032","https://openalex.org/W2148143831","https://openalex.org/W2158275940","https://openalex.org/W3120740533","https://openalex.org/W3142980988","https://openalex.org/W4285719527","https://openalex.org/W6600311290","https://openalex.org/W6603460400"],"related_works":["https://openalex.org/W4298130764","https://openalex.org/W2804364458","https://openalex.org/W2132641928","https://openalex.org/W4310225030","https://openalex.org/W2090259340","https://openalex.org/W2393816671","https://openalex.org/W2158836806","https://openalex.org/W2083665254","https://openalex.org/W1926736923","https://openalex.org/W2346511343"],"abstract_inverted_index":{"Learning":[0],"under":[1],"imbalanced":[2,91],"dataset":[3],"can":[4,74],"be":[5,75],"difficult":[6],"since":[7],"traditional":[8],"algorithms":[9],"are":[10,50],"biased":[11],"towards":[12],"the":[13,21,25,30,37,60,69,108,125,130,140,144,148],"majority":[14],"class,":[15],"providing":[16],"low":[17],"predictive":[18],"accuracy":[19],"over":[20],"minority":[22],"one.":[23],"Among":[24],"several":[26],"methods":[27,102],"proposed":[28],"in":[29,124],"literature":[31,126],"to":[32,53,106,136,159],"overcome":[33],"such":[34],"a":[35,54,65],"limitation,":[36],"most":[38],"recent":[39],"uses":[40],"multi-experts":[41],"system":[42],"(MES)":[43],"composed":[44],"of":[45,59,68],"balanced":[46,66],"classifiers,":[47],"whose":[48],"decisions":[49],"aggregated":[51],"according":[52],"combination":[55,87,98,133],"rule.":[56],"Each":[57],"classifier":[58],"MES":[61,86],"is":[62],"trained":[63],"with":[64,90],"subset":[67],"original":[70,109,149],"training":[71,150],"set,":[72],"which":[73],"determined":[76],"applying":[77],"different":[78,85],"division":[79],"methods.":[80],"This":[81],"paper":[82],"explores":[83],"how":[84],"rules":[88,134],"perform":[89],"TS,":[92,110],"experimentally":[93],"comparing":[94],"fusion":[95],"and":[96,114,119],"selection":[97,113,137,154],"criteria.":[99],"Furthermore,":[100],"two":[101],"have":[103],"been":[104],"used":[105],"divide":[107],"namely":[111],"random":[112,153],"clustering.":[115],"The":[116],"results":[117],"confirm":[118],"extend":[120],"previous":[121],"findings":[122],"reported":[123],"showing":[127],"that,":[128],"on":[129,143],"one":[131],"side,":[132,146],"belonging":[135],"framework":[138],"outperform":[139],"others":[141],"and,":[142],"other":[145],"dividing":[147],"set":[151],"via":[152],"rather":[155],"than":[156],"clustering":[157],"permits":[158],"attain":[160],"better":[161],"performance.":[162]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}
