{"id":"https://openalex.org/W3189792414","doi":"https://doi.org/10.24963/ijcai.2021/350","title":"Interpretable Minority Synthesis for Imbalanced Classification","display_name":"Interpretable Minority Synthesis for Imbalanced Classification","publication_year":2021,"publication_date":"2021-08-01","ids":{"openalex":"https://openalex.org/W3189792414","doi":"https://doi.org/10.24963/ijcai.2021/350","mag":"3189792414"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2021/350","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2021/350","pdf_url":"https://www.ijcai.org/proceedings/2021/0350.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirtieth 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/2021/0350.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100702364","display_name":"Yi He","orcid":"https://orcid.org/0000-0002-5357-6623"},"institutions":[{"id":"https://openalex.org/I79516672","display_name":"University of Louisiana at Lafayette","ror":"https://ror.org/01x8rc503","country_code":"US","type":"education","lineage":["https://openalex.org/I2799628689","https://openalex.org/I79516672"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yi He","raw_affiliation_strings":["University of Louisiana at Lafayette"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Louisiana at Lafayette","institution_ids":["https://openalex.org/I79516672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047243024","display_name":"Fudong Lin","orcid":"https://orcid.org/0000-0003-0457-2527"},"institutions":[{"id":"https://openalex.org/I79516672","display_name":"University of Louisiana at Lafayette","ror":"https://ror.org/01x8rc503","country_code":"US","type":"education","lineage":["https://openalex.org/I2799628689","https://openalex.org/I79516672"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fudong Lin","raw_affiliation_strings":["University of Louisiana at Lafayette"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Louisiana at Lafayette","institution_ids":["https://openalex.org/I79516672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061718239","display_name":"Xu Yuan","orcid":"https://orcid.org/0000-0003-3775-3033"},"institutions":[{"id":"https://openalex.org/I79516672","display_name":"University of Louisiana at Lafayette","ror":"https://ror.org/01x8rc503","country_code":"US","type":"education","lineage":["https://openalex.org/I2799628689","https://openalex.org/I79516672"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xu Yuan","raw_affiliation_strings":["University of Louisiana at Lafayette"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Louisiana at Lafayette","institution_ids":["https://openalex.org/I79516672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032851065","display_name":"Nian-Feng Tzeng","orcid":"https://orcid.org/0000-0002-8357-6632"},"institutions":[{"id":"https://openalex.org/I79516672","display_name":"University of Louisiana at Lafayette","ror":"https://ror.org/01x8rc503","country_code":"US","type":"education","lineage":["https://openalex.org/I2799628689","https://openalex.org/I79516672"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nian-Feng Tzeng","raw_affiliation_strings":["University of Louisiana at Lafayette"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Louisiana at Lafayette","institution_ids":["https://openalex.org/I79516672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I79516672"],"apc_list":null,"apc_paid":null,"fwci":1.1456,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.86308896,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2542","last_page":"2548"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":0.9983999729156494,"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"}},"topics":[{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":0.9983999729156494,"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"}},{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9962999820709229,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9944000244140625,"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/computer-science","display_name":"Computer science","score":0.7187833786010742},{"id":"https://openalex.org/keywords/oversampling","display_name":"Oversampling","score":0.641706109046936},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5937629342079163},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5716274380683899},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5625078678131104},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.5357317924499512},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5026938915252686},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.47737690806388855},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.44375744462013245},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4367330074310303},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.398210346698761},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33415281772613525},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3220910429954529}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7187833786010742},{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.641706109046936},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5937629342079163},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5716274380683899},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5625078678131104},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.5357317924499512},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5026938915252686},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.47737690806388855},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.44375744462013245},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4367330074310303},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.398210346698761},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33415281772613525},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3220910429954529},{"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2021/350","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2021/350","pdf_url":"https://www.ijcai.org/proceedings/2021/0350.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2021/350","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2021/350","pdf_url":"https://www.ijcai.org/proceedings/2021/0350.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.5600000023841858}],"awards":[{"id":"https://openalex.org/G1539379727","display_name":"III: Medium: Collaborative Research: KMELIN: Knowledge Mining and Embedding Learning for Complex Dynamic Information Networks","funder_award_id":"1763620","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6486276977","display_name":"RII Track-2 FEC: Precise Regional Forecasting via Intelligent and Rapid Harnessing of National Scale Hydrometeorological Big Data","funder_award_id":"2019511","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7605139725","display_name":"III: Small: Integrating Casual Discovery and Feature Selection with Streaming Features","funder_award_id":"1652107","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8230212441","display_name":"CRII: SaTC: Empowering Elastic-honeypot as Real-time Malicious Content Sniffers for Social Networks","funder_award_id":"1948374","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8898089434","display_name":null,"funder_award_id":"LEQSF(2018-21)-RD-A-24","funder_id":"https://openalex.org/F4320309392","funder_display_name":"Louisiana Board of Regents"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320309392","display_name":"Louisiana Board of Regents","ror":"https://ror.org/00jv89z46"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3189792414.pdf","grobid_xml":"https://content.openalex.org/works/W3189792414.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W167016754","https://openalex.org/W1959608418","https://openalex.org/W2104933073","https://openalex.org/W2148143831","https://openalex.org/W2440599146","https://openalex.org/W2490420619","https://openalex.org/W2612634114","https://openalex.org/W2737047298","https://openalex.org/W2750384547","https://openalex.org/W2753738274","https://openalex.org/W2803390034","https://openalex.org/W2884561390","https://openalex.org/W2903717923","https://openalex.org/W2963226019","https://openalex.org/W2966095869","https://openalex.org/W2977543713","https://openalex.org/W2981515171","https://openalex.org/W2986588402","https://openalex.org/W2997068998","https://openalex.org/W3034328552","https://openalex.org/W3034561829","https://openalex.org/W3034933032","https://openalex.org/W3035122597","https://openalex.org/W3107992834","https://openalex.org/W4288625073","https://openalex.org/W4320013936"],"related_works":["https://openalex.org/W2766503024","https://openalex.org/W2781247653","https://openalex.org/W4206637278","https://openalex.org/W2562263695","https://openalex.org/W2135187896","https://openalex.org/W2147201983","https://openalex.org/W2015518264","https://openalex.org/W2795035211","https://openalex.org/W2160108762","https://openalex.org/W2017034551"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,15,46,61,78],"novel":[4],"oversampling":[5],"approach":[6,27,117],"that":[7,34,66,115],"strives":[8],"to":[9,55,59,95,122],"balance":[10],"the":[11,37,41,57,102,129],"class":[12],"priors":[13],"with":[14,73,82],"considerably":[16],"imbalanced":[17,125],"data":[18],"distribution":[19],"of":[20,25,40,63,104],"high":[21],"dimensionality.":[22],"The":[23],"crux":[24],"our":[26,86,116],"lies":[28],"in":[29,142],"learning":[30],"interpretable":[31],"latent":[32],"representations":[33],"can":[35,118],"model":[36],"synthetic":[38],"mechanism":[39],"minority":[42,98,105],"samples":[43,99],"by":[44,77],"using":[45],"generative":[47],"adversarial":[48],"network(GAN).":[49],"A":[50],"Bayesian":[51],"regularizer":[52],"is":[53,133,140],"imposed":[54],"guide":[56],"GAN":[58,87],"extract":[60],"set":[62],"salient":[64],"features":[65],"are":[67,107],"either":[68],"disentangled":[69],"or":[70],"intensionally":[71],"entangled,":[72],"their":[74],"interplay":[75],"controlled":[76],"prescribed":[79],"structure,":[80],"defined":[81],"human-in-the-loop.":[83],"As":[84],"such,":[85],"enjoys":[88],"an":[89],"improved":[90],"sample":[91],"complexity,":[92],"being":[93],"able":[94],"synthesize":[96],"high-quality":[97],"even":[100],"if":[101],"sizes":[103],"classes":[106],"extremely":[108],"small":[109],"during":[110],"training.":[111],"Empirical":[112],"studies":[113],"substantiate":[114],"empower":[119],"simple":[120],"classifiers":[121],"achieve":[123],"superior":[124],"classification":[126],"performance":[127],"over":[128],"state-of-the-art":[130],"competitors":[131],"and":[132],"robust":[134],"across":[135],"various":[136],"imbalance":[137],"settings.":[138],"Code":[139],"released":[141],"github.com/fudonglin/IMSIC.":[143]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":1}],"updated_date":"2026-08-06T08:24:18.245995","created_date":"2025-10-10T00:00:00"}