{"id":"https://openalex.org/W4403791057","doi":"https://doi.org/10.1145/3664647.3680911","title":"Imbalanced Multi-instance Multi-label Learning via Coding Ensemble and Adaptive Thresholds","display_name":"Imbalanced Multi-instance Multi-label Learning via Coding Ensemble and Adaptive Thresholds","publication_year":2024,"publication_date":"2024-10-26","ids":{"openalex":"https://openalex.org/W4403791057","doi":"https://doi.org/10.1145/3664647.3680911"},"language":"en","primary_location":{"id":"doi:10.1145/3664647.3680911","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3680911","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","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":null,"display_name":"Xinyue Zhang","orcid":"https://orcid.org/0009-0003-0743-6448"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyue Zhang","raw_affiliation_strings":["National University of Defense Technology, Changsha, China"],"raw_orcid":"https://orcid.org/0009-0003-0743-6448","affiliations":[{"raw_affiliation_string":"National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074096377","display_name":"Tingjin Luo","orcid":"https://orcid.org/0000-0002-8171-3971"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tingjin Luo","raw_affiliation_strings":["National University of Defense Technology, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0002-8171-3971","affiliations":[{"raw_affiliation_string":"National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007072519","display_name":"Y.-X. Liu","orcid":"https://orcid.org/0009-0001-7338-2324"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yueying Liu","raw_affiliation_strings":["National University of Defense Technology, Changsha, China"],"raw_orcid":"https://orcid.org/0009-0001-7338-2324","affiliations":[{"raw_affiliation_string":"National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091529433","display_name":"Chenping Hou","orcid":"https://orcid.org/0000-0002-9335-0469"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenping Hou","raw_affiliation_strings":["National University of Defense Technology, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0002-9335-0469","affiliations":[{"raw_affiliation_string":"National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I170215575"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5413","last_page":"5422"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9993000030517578,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9993000030517578,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9950000047683716,"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":0.978600025177002,"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":0.7914057374000549},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5426090955734253},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.5334610939025879},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5236149430274963},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4828222692012787},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08842208981513977},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07835796475410461}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7914057374000549},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5426090955734253},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.5334610939025879},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5236149430274963},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4828222692012787},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08842208981513977},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07835796475410461}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3664647.3680911","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3680911","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1966385142","https://openalex.org/W1986490170","https://openalex.org/W2027266161","https://openalex.org/W2037248153","https://openalex.org/W2057378058","https://openalex.org/W2087341100","https://openalex.org/W2148143831","https://openalex.org/W2156935079","https://openalex.org/W2169611956","https://openalex.org/W2286081412","https://openalex.org/W2319304737","https://openalex.org/W2743273864","https://openalex.org/W2746791238","https://openalex.org/W2983438913","https://openalex.org/W3016055451","https://openalex.org/W3031232306","https://openalex.org/W3035053225","https://openalex.org/W3035054804","https://openalex.org/W3036661175","https://openalex.org/W3041664846","https://openalex.org/W3042609801","https://openalex.org/W3093220398","https://openalex.org/W3101880446","https://openalex.org/W3112079214","https://openalex.org/W3154200785","https://openalex.org/W3158777740","https://openalex.org/W3188862374","https://openalex.org/W3202106300","https://openalex.org/W3206347155","https://openalex.org/W3206354642","https://openalex.org/W4213448070","https://openalex.org/W4220731972","https://openalex.org/W4281691770","https://openalex.org/W4285492263","https://openalex.org/W4291653579","https://openalex.org/W4313827544","https://openalex.org/W4319068931","https://openalex.org/W4319586718","https://openalex.org/W4382657787","https://openalex.org/W4385571781"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Multi-instance":[0],"multi-label":[1,74],"learning":[2,75],"(MIML),":[3],"which":[4,40],"deals":[5],"with":[6,8],"objects":[7],"complex":[9],"structures":[10],"and":[11,28,85,108,155,192],"multiple":[12],"semantics,":[13],"plays":[14],"a":[15,70,93,122],"crucial":[16,42],"role":[17],"in":[18,38,61],"various":[19,201],"fields.":[20],"In":[21],"practice,":[22],"the":[23,33,56,81,99,115,119,130,166,186,204],"naturally":[24],"skewed":[25],"label":[26,29,36,177],"distribution":[27],"dependence":[30],"contribute":[31],"to":[32,55,97,113,151,161],"issue":[34],"of":[35,58,102,133,206],"imbalance":[37,167],"MIML,":[39],"is":[41,126],"but":[43],"rarely":[44],"studied.":[45],"Most":[46],"existing":[47],"MIML":[48],"methods":[49],"often":[50],"produce":[51],"biased":[52],"models":[53],"due":[54],"ignorance":[57],"inter-class":[59],"variations":[60],"imbalanced":[62,72,120],"data.":[63],"To":[64],"address":[65],"this":[66],"issue,":[67],"we":[68,91],"propose":[69],"novel":[71,123],"multi-instance":[73],"method":[76,96],"named":[77],"IMIMLC,":[78],"based":[79],"on":[80,143,200],"error-correcting":[82,131],"coding":[83],"ensemble":[84,124],"an":[86],"adaptive":[87],"threshold":[88],"strategy.":[89],"Specifically,":[90],"design":[92],"feature":[94],"embedding":[95],"extract":[98],"structural":[100],"information":[101],"each":[103,175],"object":[104],"via":[105],"Fisher":[106],"vectors":[107],"eliminate":[109],"inexact":[110],"supervision.":[111],"Subsequently,":[112],"alleviate":[114],"disturbance":[116],"caused":[117,184],"by":[118,128,148,178,185],"distribution,":[121],"model":[125,163],"constructed":[127],"concatenating":[129],"codes":[132,150],"randomly":[134],"selected":[135],"subtasks.":[136],"Meanwhile,":[137],"IMIMLC":[138,170,207],"trains":[139],"binary":[140],"base":[141],"classifiers":[142],"small-scale":[144],"data":[145],"blocks":[146],"partitioned":[147],"our":[149],"enhance":[152],"their":[153,193],"diversity":[154],"then":[156],"learns":[157,172],"more":[158],"reliable":[159],"results":[160,199],"improve":[162],"robustness":[164],"for":[165,174],"issue.":[168],"Furthermore,":[169],"adaptively":[171],"thresholds":[173],"individual":[176],"margin":[179],"maximization,":[180],"preventing":[181],"inaccurate":[182],"predictions":[183],"semantic":[187],"discrepancy":[188],"across":[189],"many":[190],"labels":[191],"unbalanced":[194],"ratios.":[195],"Finally,":[196],"extensive":[197],"experimental":[198],"datasets":[202],"validate":[203],"effectiveness":[205],"against":[208],"state-of-the-art":[209],"approaches.":[210]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3}],"updated_date":"2026-07-18T07:39:51.176621","created_date":"2025-10-10T00:00:00"}