{"id":"https://openalex.org/W2170081481","doi":"https://doi.org/10.1145/1557019.1557117","title":"Quantification and semi-supervised classification methods for handling changes in class distribution","display_name":"Quantification and semi-supervised classification methods for handling changes in class distribution","publication_year":2009,"publication_date":"2009-06-28","ids":{"openalex":"https://openalex.org/W2170081481","doi":"https://doi.org/10.1145/1557019.1557117","mag":"2170081481"},"language":"en","primary_location":{"id":"doi:10.1145/1557019.1557117","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1557019.1557117","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},"type":"conference-paper","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/A5055882507","display_name":"Jack Chongjie Xue","orcid":null},"institutions":[{"id":"https://openalex.org/I164389053","display_name":"Fordham University","ror":"https://ror.org/03qnxaf80","country_code":"US","type":"education","lineage":["https://openalex.org/I164389053"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jack Chongjie Xue","raw_affiliation_strings":["Fordham University, Bronx, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fordham University, Bronx, NY, USA","institution_ids":["https://openalex.org/I164389053"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073269476","display_name":"Gary M. Weiss","orcid":"https://orcid.org/0000-0001-5009-7101"},"institutions":[{"id":"https://openalex.org/I164389053","display_name":"Fordham University","ror":"https://ror.org/03qnxaf80","country_code":"US","type":"education","lineage":["https://openalex.org/I164389053"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gary M. Weiss","raw_affiliation_strings":["Fordham University, Bronx, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fordham University, Bronx, NY, USA","institution_ids":["https://openalex.org/I164389053"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I164389053"],"apc_list":null,"apc_paid":null,"fwci":2.6808,"has_fulltext":false,"cited_by_count":45,"citation_normalized_percentile":{"value":0.91434348,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"897","last_page":"906"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9990000128746033,"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":0.9990000128746033,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9480000138282776,"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/T10057","display_name":"Face and Expression Recognition","score":0.9448999762535095,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/class","display_name":"Class (philosophy)","score":0.6372005343437195},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6048922538757324},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5221883654594421},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48358672857284546},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.4291137158870697},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38017988204956055},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.328891396522522},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26674526929855347}],"concepts":[{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.6372005343437195},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6048922538757324},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5221883654594421},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48358672857284546},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.4291137158870697},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38017988204956055},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.328891396522522},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26674526929855347},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/1557019.1557117","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1557019.1557117","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.148.8725","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.8725","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/kdd09.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":29,"referenced_works":["https://openalex.org/W139044672","https://openalex.org/W167016754","https://openalex.org/W748373178","https://openalex.org/W1479807131","https://openalex.org/W1488030573","https://openalex.org/W1504694836","https://openalex.org/W1506338521","https://openalex.org/W1511132754","https://openalex.org/W1524761913","https://openalex.org/W1570448133","https://openalex.org/W1965895350","https://openalex.org/W1972047492","https://openalex.org/W1977245551","https://openalex.org/W1980093325","https://openalex.org/W2096942889","https://openalex.org/W2114968414","https://openalex.org/W2118443521","https://openalex.org/W2125055259","https://openalex.org/W2136504847","https://openalex.org/W2141253686","https://openalex.org/W2148143831","https://openalex.org/W2531563875","https://openalex.org/W2913668833","https://openalex.org/W2966207845","https://openalex.org/W2997149703","https://openalex.org/W3120740533","https://openalex.org/W3202461034","https://openalex.org/W4255951738","https://openalex.org/W4301013721"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4224009465","https://openalex.org/W4306674287","https://openalex.org/W4286629047","https://openalex.org/W4205958290","https://openalex.org/W4384212932","https://openalex.org/W2368454205","https://openalex.org/W4390590544","https://openalex.org/W2096195258","https://openalex.org/W2990460313"],"abstract_inverted_index":{"In":[0,39],"realistic":[1],"settings":[2],"the":[3,19,22,29,44,47,58,76,85,89,93,98,111,114],"prevalence":[4],"of":[5,21,67,78,88,149],"a":[6,11,65,107,128,147],"class":[7,48,86,123],"may":[8],"change":[9],"after":[10],"classifier":[12,100,109],"is":[13,28,34],"induced":[14],"and":[15,37,51,63,74,96,135,144,164],"this":[16,26,40,72],"will":[17],"degrade":[18],"performance":[20,77],"classifier.":[23],"Further":[24],"complicating":[25],"scenario":[27],"fact":[30],"that":[31,131,156],"labeled":[32],"data":[33,91,151],"often":[35],"scarce":[36],"expensive.":[38],"paper":[41],"we":[42],"address":[43],"problem":[45,73],"where":[46],"distribution":[49,87,95,117],"changes":[50],"only":[52],"unlabeled":[53,90],"examples":[54,112],"are":[55,119,140],"available":[56],"from":[57,92,113],"new":[59,108,115],"distribution.":[60],"We":[61,125],"design":[62],"evaluate":[64],"number":[66],"methods":[68,83,105,139,158],"for":[69],"coping":[70],"with":[71,121],"compare":[75],"these":[79],"methods.":[80],"Our":[81,153],"quantification-based":[82],"estimate":[84],"changed":[94],"adjust":[97],"original":[99],"accordingly,":[101],"while":[102],"our":[103,157],"semi-supervised":[104,136],"build":[106],"using":[110,142],"(unlabeled)":[116],"which":[118],"supplemented":[120],"predicted":[122],"values.":[124],"also":[126],"introduce":[127],"hybrid":[129],"method":[130],"utilizes":[132],"both":[133],"quantification":[134],"learning.":[137],"All":[138],"evaluated":[141],"accuracy":[143,163],"F-measure":[145],"on":[146],"set":[148],"benchmark":[150],"sets.":[152],"results":[154],"demonstrate":[155],"yield":[159],"substantial":[160],"improvements":[161],"in":[162],"F-measure.":[165]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":4},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
