{"id":"https://openalex.org/W2165837041","doi":"https://doi.org/10.1109/ijcnn.2010.5596486","title":"Cost-sensitive learning methods for imbalanced data","display_name":"Cost-sensitive learning methods for imbalanced data","publication_year":2010,"publication_date":"2010-07-01","ids":{"openalex":"https://openalex.org/W2165837041","doi":"https://doi.org/10.1109/ijcnn.2010.5596486","mag":"2165837041"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2010.5596486","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2010.5596486","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 2010 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/A5081419354","display_name":"Nguyen Thai-Nghe","orcid":"https://orcid.org/0000-0002-9127-2778"},"institutions":[{"id":"https://openalex.org/I155765044","display_name":"University of Hildesheim","ror":"https://ror.org/02f9det96","country_code":"DE","type":"education","lineage":["https://openalex.org/I155765044"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Nguyen Thai-Nghe","raw_affiliation_strings":["Information Systems and Machine Learning Laboratory, University of Hildesheim, Hildesheim, Germany"],"affiliations":[{"raw_affiliation_string":"Information Systems and Machine Learning Laboratory, University of Hildesheim, Hildesheim, Germany","institution_ids":["https://openalex.org/I155765044"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019633739","display_name":"Zeno Gantner","orcid":null},"institutions":[{"id":"https://openalex.org/I155765044","display_name":"University of Hildesheim","ror":"https://ror.org/02f9det96","country_code":"DE","type":"education","lineage":["https://openalex.org/I155765044"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Zeno Gantner","raw_affiliation_strings":["Information Systems and Machine Learning Laboratory, University of Hildesheim, Hildesheim, Germany"],"affiliations":[{"raw_affiliation_string":"Information Systems and Machine Learning Laboratory, University of Hildesheim, Hildesheim, Germany","institution_ids":["https://openalex.org/I155765044"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039470755","display_name":"Lars Schmidt-Thieme","orcid":"https://orcid.org/0000-0001-5729-6023"},"institutions":[{"id":"https://openalex.org/I155765044","display_name":"University of Hildesheim","ror":"https://ror.org/02f9det96","country_code":"DE","type":"education","lineage":["https://openalex.org/I155765044"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Lars Schmidt-Thieme","raw_affiliation_strings":["Information Systems and Machine Learning Laboratory, University of Hildesheim, Hildesheim, Germany"],"affiliations":[{"raw_affiliation_string":"Information Systems and Machine Learning Laboratory, University of Hildesheim, Hildesheim, Germany","institution_ids":["https://openalex.org/I155765044"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5081419354"],"corresponding_institution_ids":["https://openalex.org/I155765044"],"apc_list":null,"apc_paid":null,"fwci":13.0574,"has_fulltext":false,"cited_by_count":349,"citation_normalized_percentile":{"value":0.9876051,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"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":0.9937999844551086,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.984499990940094,"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":0.8099092841148376},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.7809024453163147},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7451014518737793},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6766178011894226},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6314573287963867},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5520418882369995},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4836229383945465},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4706287384033203},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.44096454977989197}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8099092841148376},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.7809024453163147},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7451014518737793},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6766178011894226},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6314573287963867},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5520418882369995},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4836229383945465},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4706287384033203},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44096454977989197},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/ijcnn.2010.5596486","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2010.5596486","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 2010 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.165.9960","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.9960","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ismll.uni-hildesheim.de/pub/pdfs/Nguyen_et_al_IJCNN2010_CSL.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.180.8890","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.180.8890","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ismll.uni-hildesheim.de/pub/pdfs/Nguyen_et_al_IJCNN2010_CSL.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":62,"referenced_works":["https://openalex.org/W83133245","https://openalex.org/W85350352","https://openalex.org/W102369970","https://openalex.org/W146202706","https://openalex.org/W163887238","https://openalex.org/W167016754","https://openalex.org/W1490989942","https://openalex.org/W1506588750","https://openalex.org/W1551909886","https://openalex.org/W1558666744","https://openalex.org/W1567649426","https://openalex.org/W1570448133","https://openalex.org/W1591261915","https://openalex.org/W1826478421","https://openalex.org/W1838291615","https://openalex.org/W1841999836","https://openalex.org/W1973682096","https://openalex.org/W1977640460","https://openalex.org/W1987859285","https://openalex.org/W1993220166","https://openalex.org/W1994410331","https://openalex.org/W1999318832","https://openalex.org/W2023299139","https://openalex.org/W2040424159","https://openalex.org/W2053724458","https://openalex.org/W2058732827","https://openalex.org/W2070808135","https://openalex.org/W2100894633","https://openalex.org/W2104167780","https://openalex.org/W2107686700","https://openalex.org/W2109943925","https://openalex.org/W2112076978","https://openalex.org/W2118963717","https://openalex.org/W2118978333","https://openalex.org/W2119168155","https://openalex.org/W2126734246","https://openalex.org/W2137959503","https://openalex.org/W2141921295","https://openalex.org/W2143534004","https://openalex.org/W2148143831","https://openalex.org/W2171629518","https://openalex.org/W2616891418","https://openalex.org/W2979832949","https://openalex.org/W3102221121","https://openalex.org/W4300498324","https://openalex.org/W6603308255","https://openalex.org/W6603460400","https://openalex.org/W6604223235","https://openalex.org/W6605992286","https://openalex.org/W6606740957","https://openalex.org/W6629597952","https://openalex.org/W6630208708","https://openalex.org/W6633119224","https://openalex.org/W6633571276","https://openalex.org/W6635474240","https://openalex.org/W6638683995","https://openalex.org/W6664464457","https://openalex.org/W6676769703","https://openalex.org/W6678748019","https://openalex.org/W6681322979","https://openalex.org/W6682141768","https://openalex.org/W6684850183"],"related_works":["https://openalex.org/W2052515325","https://openalex.org/W2050948537","https://openalex.org/W2767646790","https://openalex.org/W2352041579","https://openalex.org/W1488006380","https://openalex.org/W1998176685","https://openalex.org/W2138381686","https://openalex.org/W2141585124","https://openalex.org/W3123883311","https://openalex.org/W2032175896"],"abstract_inverted_index":{"Class":[0],"imbalance":[1,72],"is":[2,32],"one":[3],"of":[4,54,59],"the":[5,23,28,102,116,121,126,130,135],"challenging":[6],"problems":[7],"for":[8],"machine":[9],"learning":[10,13,51],"algorithms.":[11],"When":[12],"from":[14,115],"highly":[15],"imbalanced":[16,113],"data,":[17],"most":[18,53],"classifiers":[19],"are":[20],"overwhelmed":[21],"by":[22,100],"majority":[24],"class":[25,71],"examples,":[26],"so":[27],"false":[29],"negative":[30],"rate":[31],"always":[33],"high.":[34],"Although":[35],"researchers":[36],"have":[37],"introduced":[38],"many":[39],"methods":[40,67],"to":[41],"deal":[42,69],"with":[43,70,87],"this":[44],"problem,":[45],"including":[46],"resampling":[47,75],"techniques":[48,86],"and":[49,76,82,129],"cost-sensitive":[50],"(CSL),":[52],"them":[55],"focus":[56],"on":[57,111],"either":[58],"these":[60],"techniques.":[61],"This":[62],"study":[63],"presents":[64],"two":[65],"empirical":[66],"that":[68,120],"using":[73,89,98],"both":[74],"CSL.":[77],"The":[78,94],"first":[79,122],"method":[80,96,123,132],"combines":[81],"compares":[83],"several":[84],"sampling":[85],"CSL":[88,99],"support":[90],"vector":[91],"machines":[92],"(SVM).":[93],"second":[95,131],"proposes":[97],"optimizing":[101],"cost":[103],"ratio":[104],"(cost":[105],"matrix)":[106],"locally.":[107],"Our":[108],"experimental":[109],"results":[110],"18":[112],"datasets":[114],"UCI":[117],"repository":[118],"show":[119],"can":[124,133],"reduce":[125],"misclassification":[127],"costs,":[128],"improve":[134],"classifier":[136],"performance.":[137]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":28},{"year":2024,"cited_by_count":32},{"year":2023,"cited_by_count":36},{"year":2022,"cited_by_count":37},{"year":2021,"cited_by_count":50},{"year":2020,"cited_by_count":44},{"year":2019,"cited_by_count":23},{"year":2018,"cited_by_count":18},{"year":2017,"cited_by_count":13},{"year":2016,"cited_by_count":14},{"year":2015,"cited_by_count":14},{"year":2014,"cited_by_count":7},{"year":2013,"cited_by_count":14},{"year":2012,"cited_by_count":8}],"updated_date":"2026-04-13T07:58:08.660418","created_date":"2025-10-10T00:00:00"}