{"id":"https://openalex.org/W4408345753","doi":"https://doi.org/10.1109/icassp49660.2025.10888549","title":"A Margin-Maximizing Fine-Grained Ensemble Method","display_name":"A Margin-Maximizing Fine-Grained Ensemble Method","publication_year":2025,"publication_date":"2025-03-12","ids":{"openalex":"https://openalex.org/W4408345753","doi":"https://doi.org/10.1109/icassp49660.2025.10888549"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49660.2025.10888549","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10888549","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":"Jinghui Yuan","orcid":null},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinghui Yuan","raw_affiliation_strings":["Northwestern Polytechnical University,School of Artificial Intelligence, OPtics and ElectroNics (iOPEN),Shaanxi,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University,School of Artificial Intelligence, OPtics and ElectroNics (iOPEN),Shaanxi,China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100353573","display_name":"Hao Chen","orcid":"https://orcid.org/0000-0002-6282-0237"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Chen","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Queen Mary School Hainan,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Queen Mary School Hainan,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114400505","display_name":"Renwei Luo","orcid":null},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Renwei Luo","raw_affiliation_strings":["Northwestern Polytechnical University,School of Artificial Intelligence, OPtics and ElectroNics (iOPEN),Shaanxi,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University,School of Artificial Intelligence, OPtics and ElectroNics (iOPEN),Shaanxi,China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003222421","display_name":"Feiping Nie","orcid":"https://orcid.org/0000-0002-0871-6519"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feiping Nie","raw_affiliation_strings":["Northwestern Polytechnical University,School of Artificial Intelligence, OPtics and ElectroNics (iOPEN),Shaanxi,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University,School of Artificial Intelligence, OPtics and ElectroNics (iOPEN),Shaanxi,China","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11595","display_name":"Textile materials and evaluations","score":0.48240000009536743,"subfield":{"id":"https://openalex.org/subfields/2507","display_name":"Polymers and Plastics"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11595","display_name":"Textile materials and evaluations","score":0.48240000009536743,"subfield":{"id":"https://openalex.org/subfields/2507","display_name":"Polymers and Plastics"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13049","display_name":"Surface Roughness and Optical Measurements","score":0.4431999921798706,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.4032999873161316,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.7780761122703552},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6222001910209656},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.17931050062179565}],"concepts":[{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.7780761122703552},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6222001910209656},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.17931050062179565}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49660.2025.10888549","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10888549","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.4699999988079071,"display_name":"Zero hunger"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W2034996255","https://openalex.org/W2063375278","https://openalex.org/W2104652648","https://openalex.org/W2105372494","https://openalex.org/W2106115875","https://openalex.org/W2121434426","https://openalex.org/W2125464731","https://openalex.org/W2135850590","https://openalex.org/W2295598076","https://openalex.org/W2498672755","https://openalex.org/W2622577666","https://openalex.org/W2736299552","https://openalex.org/W2803590506","https://openalex.org/W2989917577","https://openalex.org/W3004059350","https://openalex.org/W3011304563","https://openalex.org/W3011573842","https://openalex.org/W3037281092","https://openalex.org/W3047611651","https://openalex.org/W3109408437","https://openalex.org/W3169203486","https://openalex.org/W3180725527","https://openalex.org/W4250589301","https://openalex.org/W4292382515","https://openalex.org/W4318677156","https://openalex.org/W4385605940","https://openalex.org/W4386648688","https://openalex.org/W4391847287","https://openalex.org/W4392903263","https://openalex.org/W4400767531","https://openalex.org/W4401155788","https://openalex.org/W6604680514","https://openalex.org/W6748458661","https://openalex.org/W6873338388","https://openalex.org/W6873859288"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W3125011624","https://openalex.org/W1508631387","https://openalex.org/W2370917603","https://openalex.org/W2390279801","https://openalex.org/W2952760143","https://openalex.org/W4391913857","https://openalex.org/W2358668433"],"abstract_inverted_index":{"Ensemble":[0,29],"learning":[1],"has":[2],"achieved":[3],"remarkable":[4],"success":[5],"in":[6,19],"machine":[7],"learning,":[8],"but":[9],"its":[10,17],"reliance":[11],"on":[12,110],"numerous":[13],"base":[14,143],"learners":[15,44,144],"limits":[16],"application":[18],"resource-constrained":[20],"environments.":[21],"This":[22,88],"paper":[23],"introduces":[24],"an":[25,114],"innovative":[26],"\"Margin-Maximizing":[27],"Fine-Grained":[28],"Method\"":[30],"that":[31,102,119,131],"achieves":[32],"performance":[33],"surpassing":[34],"large-scale":[35],"ensembles":[36],"by":[37],"meticulously":[38],"optimizing":[39],"a":[40,51,73,78],"small":[41],"number":[42],"of":[43,67,141],"and":[45,80,94,123,145],"enhancing":[46],"generalization":[47],"capability.":[48],"We":[49],"propose":[50],"novel":[52],"learnable":[53],"confidence":[54,59,97],"matrix,":[55],"quantifying":[56],"each":[57,61],"classifier\u2019s":[58],"for":[60],"category,":[62],"precisely":[63],"capturing":[64],"category-specific":[65],"advantages":[66],"individual":[68],"learners.":[69],"Furthermore,":[70],"we":[71,100,112],"design":[72],"margin-based":[74],"loss":[75,104],"function,":[76],"constructing":[77],"smooth":[79],"partially":[81],"convex":[82],"objective":[83],"using":[84,138],"the":[85,103,142],"logsumexp":[86],"technique.":[87],"approach":[89],"improves":[90],"optimization,":[91],"eases":[92],"convergence,":[93],"enables":[95],"adaptive":[96],"allocation.":[98],"Finally,":[99],"prove":[101],"function":[105],"is":[106],"Lipschitz":[107],"continuous,":[108],"based":[109],"which":[111],"develop":[113],"efficient":[115],"gradient":[116],"optimization":[117],"algorithm":[118],"simultaneously":[120],"maximizes":[121],"margins":[122],"dynamically":[124],"adjusts":[125],"learner":[126],"weights.":[127],"Extensive":[128],"experiments":[129],"demonstrate":[130],"our":[132],"method":[133],"outperforms":[134],"traditional":[135],"random":[136],"forests":[137],"only":[139],"one-tenth":[140],"other":[146],"state-of-the-art":[147],"ensemble":[148],"methods.":[149]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
