{"id":"https://openalex.org/W2741731870","doi":"https://doi.org/10.24963/ijcai.2017/283","title":"Learning Latest Classifiers without Additional Labeled Data","display_name":"Learning Latest Classifiers without Additional Labeled Data","publication_year":2017,"publication_date":"2017-07-28","ids":{"openalex":"https://openalex.org/W2741731870","doi":"https://doi.org/10.24963/ijcai.2017/283","mag":"2741731870"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2017/283","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2017/283","pdf_url":"https://www.ijcai.org/proceedings/2017/0283.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2017/0283.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5030880294","display_name":"Atsutoshi Kumagai","orcid":"https://orcid.org/0000-0002-2915-4615"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Atsutoshi Kumagai","raw_affiliation_strings":["NTT Corporation, NTT Secure Platform Laboratories, NTT"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Corporation, NTT Secure Platform Laboratories, NTT","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034538103","display_name":"Tomoharu Iwata","orcid":"https://orcid.org/0000-0003-4425-1971"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tomoharu Iwata","raw_affiliation_strings":["NTT Corporation, NTT Communication Science Laboratories, NTT"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Corporation, NTT Communication Science Laboratories, NTT","institution_ids":["https://openalex.org/I2251713219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2251713219"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2039","last_page":"2045"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining 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/T12761","display_name":"Data Stream Mining 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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9986000061035156,"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/T11644","display_name":"Spam and Phishing Detection","score":0.9983999729156494,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7437747716903687},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6906360983848572},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6554038524627686},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6503182649612427},{"id":"https://openalex.org/keywords/retraining","display_name":"Retraining","score":0.5883746147155762},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.5785646438598633},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.5657148957252502},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5652326345443726},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5611366033554077},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4888896048069},{"id":"https://openalex.org/keywords/random-subspace-method","display_name":"Random subspace method","score":0.48183169960975647},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40512531995773315}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7437747716903687},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6906360983848572},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6554038524627686},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6503182649612427},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.5883746147155762},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.5785646438598633},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.5657148957252502},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5652326345443726},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5611366033554077},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4888896048069},{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.48183169960975647},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40512531995773315},{"id":"https://openalex.org/C155202549","wikidata":"https://www.wikidata.org/wiki/Q178803","display_name":"International trade","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2017/283","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2017/283","pdf_url":"https://www.ijcai.org/proceedings/2017/0283.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2017/283","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2017/283","pdf_url":"https://www.ijcai.org/proceedings/2017/0283.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2741731870.pdf","grobid_xml":"https://content.openalex.org/works/W2741731870.grobid-xml"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W1905487287","https://openalex.org/W1966771059","https://openalex.org/W1977005232","https://openalex.org/W1993871915","https://openalex.org/W2009086942","https://openalex.org/W2031214791","https://openalex.org/W2034368206","https://openalex.org/W2099419573","https://openalex.org/W2103851188","https://openalex.org/W2108018401","https://openalex.org/W2108501770","https://openalex.org/W2111121652","https://openalex.org/W2112483442","https://openalex.org/W2133088989","https://openalex.org/W2133491790","https://openalex.org/W2162772535","https://openalex.org/W2163310171","https://openalex.org/W2165698076","https://openalex.org/W2169075655","https://openalex.org/W2170935389","https://openalex.org/W2187089797","https://openalex.org/W2293363371","https://openalex.org/W2470412537","https://openalex.org/W4235130247","https://openalex.org/W4301518628"],"related_works":["https://openalex.org/W2130553454","https://openalex.org/W3022007134","https://openalex.org/W4317548404","https://openalex.org/W2087783760","https://openalex.org/W1509924131","https://openalex.org/W4287241967","https://openalex.org/W3144173820","https://openalex.org/W3104108945","https://openalex.org/W209733029","https://openalex.org/W3163689946"],"abstract_inverted_index":{"In":[0,33,127],"various":[1],"applications":[2],"such":[3],"as":[4,54,56,160,162],"spam":[5],"mail":[6],"classification,":[7],"the":[8,24,65,69,79,86,89,92,101,109,112,124,129,133,143,150,166,172,179,184,204,210],"performance":[9,66],"of":[10,71,88,115,153,175,209],"classifiers":[11,17,42,103],"deteriorates":[12],"over":[13],"time.":[14],"Although":[15],"retraining":[16],"using":[18,44,123,142,217],"labeled":[19,28,57,144],"data":[20,29,58,221],"helps":[21],"to":[22,40,52],"maintain":[23],"performance,":[25],"continuously":[26],"preparing":[27],"is":[30,68,85,121,213],"quite":[31],"expensive.":[32],"this":[34],"paper,":[35],"we":[36,193],"propose":[37],"a":[38,154,195],"method":[39,99,131,212],"learn":[41],"by":[43,141,169],"newly":[45],"obtained":[46],"unlabeled":[47,125,146],"data,":[48],"which":[49,156],"are":[50],"easy":[51],"prepare,":[53],"well":[55,161],"collected":[59],"beforehand.":[60],"A":[61],"major":[62,83],"reason":[63,84],"for":[64],"deterioration":[67],"emergence":[70],"new":[72,116,158,176,190,199],"features":[73,117,120,159,177,200],"that":[74,104,197],"do":[75],"not":[76],"appear":[77],"in":[78],"training":[80,93,137],"phase.":[81,206],"Another":[82],"change":[87],"distribution":[90,114,174],"between":[91,136],"and":[94,138,145,178,201,219],"test":[95,139,167,205],"phases.":[96],"The":[97,207],"proposed":[98,110,130,211],"learns":[100],"latest":[102],"overcome":[105],"both":[106,171],"problems.":[107],"With":[108],"method,":[111],"conditional":[113,173],"given":[118],"existing":[119,163],"learned":[122],"data.":[126,147],"addition,":[128],"estimates":[132],"density":[134],"ratio":[135],"distributions":[140],"We":[148],"approximate":[149],"classification":[151],"error":[152,186],"classifier,":[155],"exploits":[157,198],"features,":[164],"at":[165],"phase":[168],"incorporating":[170],"densityratio,":[180],"simultaneously.":[181],"By":[182],"minimizing":[183],"approximated":[185],"while":[187],"integrating":[188],"out":[189],"feature":[191],"values,":[192],"obtain":[194],"classifier":[196],"fits":[202],"on":[203],"effectiveness":[208],"demonstrated":[214],"with":[215],"experiments":[216],"synthetic":[218],"real-world":[220],"sets.":[222]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
