{"id":"https://openalex.org/W218853445","doi":null,"title":"Cost-Sensitive Learning vs. Sampling: Which is Best for Handling Unbalanced Classes with Unequal Error Costs?","display_name":"Cost-Sensitive Learning vs. Sampling: Which is Best for Handling Unbalanced Classes with Unequal Error Costs?","publication_year":2007,"publication_date":"2007-01-01","ids":{"openalex":"https://openalex.org/W218853445","mag":"218853445"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.72.9601","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.72.9601","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://storm.cis.fordham.edu/~gweiss/papers/dmin07-weiss.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/A5073269476","display_name":"Gary M. Weiss","orcid":"https://orcid.org/0000-0001-5009-7101","observed_orcids":["https://orcid.org/0000-0001-5009-7101"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gary M. Weiss","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-5009-7101","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070759267","display_name":"Kate McCarthy","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kate McCarthy","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5028899732","display_name":"Bibi Zabar","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bibi Zabar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.2963,"has_fulltext":false,"cited_by_count":234,"citation_normalized_percentile":{"value":0.93860756,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"35","last_page":"41"},"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":0.00019999999494757503,"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":0.0,"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/cost-sensitive-learning","display_name":"cost-sensitive learning","score":0.9919999837875366},{"id":"https://openalex.org/keywords/class-imbalance","display_name":"class imbalance","score":0.734000027179718},{"id":"https://openalex.org/keywords/oversampling","display_name":"oversampling","score":0.9430000185966492},{"id":"https://openalex.org/keywords/undersampling","display_name":"undersampling","score":1.0},{"id":"https://openalex.org/keywords/minority-class","display_name":"minority class","score":0.7960000038146973},{"id":"https://openalex.org/keywords/decision-tree","display_name":"decision tree","score":0.9120000004768372},{"id":"https://openalex.org/keywords/classifier-comparison","display_name":"classifier comparison","score":0.44699999690055847}],"concepts":[{"id":"https://openalex.org/C136536468","wikidata":"https://www.wikidata.org/wiki/Q1225894","display_name":"Undersampling","level":2,"score":0.9331470131874084},{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.8482630252838135},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.666379988193512},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6489588022232056},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5877189636230469},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5512604713439941},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.483994722366333},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.45930469036102295},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4414237439632416},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3533569574356079},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32225725054740906},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22744286060333252},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.72.9601","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.72.9601","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://storm.cis.fordham.edu/~gweiss/papers/dmin07-weiss.pdf","raw_type":"text"},{"id":"mag:218853445","doi":null,"is_oa":false,"landing_page_url":"https://storm.cis.fordham.edu/gweiss/papers/dmin07-weiss.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306508216","display_name":"DMIN","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"DMIN","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.4074000120162964}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W85350352","https://openalex.org/W145450961","https://openalex.org/W167016754","https://openalex.org/W1594031697","https://openalex.org/W1941659294","https://openalex.org/W2098576843","https://openalex.org/W2114968414","https://openalex.org/W2123977051","https://openalex.org/W2125055259","https://openalex.org/W2136865157","https://openalex.org/W2136903812","https://openalex.org/W2148143831","https://openalex.org/W2149706766","https://openalex.org/W2165613952","https://openalex.org/W2169347018","https://openalex.org/W2182254868","https://openalex.org/W3085162807"],"related_works":["https://openalex.org/W1971828037","https://openalex.org/W2133928392","https://openalex.org/W2955181833","https://openalex.org/W3017086986","https://openalex.org/W2366172594","https://openalex.org/W1925587639","https://openalex.org/W2163735391","https://openalex.org/W2171629518","https://openalex.org/W2120040939","https://openalex.org/W2187626490"],"abstract_inverted_index":{"Abstract-":[0],"The":[1,100],"classifier":[2],"built":[3],"from":[4],"a":[5,9,70,92],"data":[6,89,124],"set":[7],"with":[8,88],"highly":[10],"skewed":[11,93],"class":[12,60,63,94,158],"distribution":[13,95],"generally":[14],"predicts":[15],"the":[16,25,34,58,62,76,104,108,112,122,133,144],"more":[17,22,125],"frequently":[18],"occurring":[19,27],"classes":[20],"much":[21,71],"often":[23],"than":[24,75],"infrequently":[26],"classes.":[28],"This":[29],"is":[30,55,61],"largely":[31],"due":[32],"to":[33,41,120,140],"fact":[35],"that":[36,90],"most":[37],"classifiers":[38],"are":[39],"designed":[40],"maximize":[42],"accuracy.":[43],"In":[44,79,127],"many":[45],"instances,":[46],"such":[47],"as":[48],"for":[49,86],"medical":[50],"diagnosis,":[51],"this":[52,80,128],"classification":[53],"behavior":[54],"unacceptable":[56],"because":[57],"minority":[59],"of":[64,135],"primary":[65],"interest":[66],"(i.e.,":[67],"it":[68],"has":[69,91],"higher":[72],"misclassification":[73,98,105],"cost":[74],"majority":[77],"class).":[78],"paper":[81,129],"we":[82,130],"compare":[83,132],"three":[84],"methods":[85,115,137],"dealing":[87],"and":[96],"nonuniform":[97],"costs.":[99],"first":[101],"method":[102],"incorporates":[103],"costs":[106],"into":[107],"learning":[109],"algorithm":[110],"while":[111],"other":[113],"two":[114],"employ":[116],"oversampling":[117],"or":[118],"undersampling":[119],"make":[121],"training":[123],"balanced.":[126],"empirically":[131],"effectiveness":[134],"these":[136],"in":[138],"order":[139],"determine":[141],"which":[142],"produces":[143],"best":[145],"overall":[146],"classifier\u2014and":[147],"under":[148],"what":[149],"circumstances.":[150],"Keywords:":[151],"Cost-sensitive":[152],"learning,":[153],"sampling,":[154],"classification,":[155],"decision":[156],"trees,":[157],"imbalance.":[159]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":17},{"year":2022,"cited_by_count":19},{"year":2021,"cited_by_count":29},{"year":2020,"cited_by_count":26},{"year":2019,"cited_by_count":21},{"year":2018,"cited_by_count":23},{"year":2017,"cited_by_count":16},{"year":2016,"cited_by_count":8},{"year":2015,"cited_by_count":11},{"year":2014,"cited_by_count":14},{"year":2013,"cited_by_count":8},{"year":2012,"cited_by_count":8}],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2016-06-24T00:00:00"}