{"id":"https://openalex.org/W7119530788","doi":"https://doi.org/10.3390/make8010017","title":"MBS: A Modality-Balanced Strategy for Multimodal Sample Selection","display_name":"MBS: A Modality-Balanced Strategy for Multimodal Sample Selection","publication_year":2026,"publication_date":"2026-01-08","ids":{"openalex":"https://openalex.org/W7119530788","doi":"https://doi.org/10.3390/make8010017"},"language":"en","primary_location":{"id":"doi:10.3390/make8010017","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make8010017","pdf_url":"https://www.mdpi.com/2504-4990/8/1/17/pdf?version=1767885893","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-4990/8/1/17/pdf?version=1767885893","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yuntao Xu","orcid":"https://orcid.org/0000-0002-3757-7482"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuntao Xu","raw_affiliation_strings":["College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 211106, China"],"raw_orcid":"https://orcid.org/0000-0002-3757-7482","affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 211106, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122035819","display_name":"Bing Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Bing Chen","raw_affiliation_strings":["College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 211106, China"],"raw_orcid":"https://orcid.org/0000-0002-2863-5441","affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 211106, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122341811","display_name":"Feng Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Hu","raw_affiliation_strings":["College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 211106, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 211106, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122318533","display_name":"Jiawei Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiawei Liu","raw_affiliation_strings":["College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 211106, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 211106, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014650800","display_name":"C. L. Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changjie Zhao","raw_affiliation_strings":["College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 211106, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 211106, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122377174","display_name":"Hongtao Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongtao Wu","raw_affiliation_strings":["College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 211106, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 211106, China","institution_ids":["https://openalex.org/I9842412"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5122035819"],"corresponding_institution_ids":["https://openalex.org/I9842412"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1559},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1559},"fwci":7.2709,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.92622272,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"8","issue":"1","first_page":"17","last_page":"17"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.08919999748468399,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.08919999748468399,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.08669999986886978,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0714000016450882,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.6154000163078308},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.545199990272522},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5098000168800354},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5016999840736389},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4982999861240387},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.49300000071525574},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.48739999532699585}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.720300018787384},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.6154000163078308},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5946000218391418},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5741000175476074},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.545199990272522},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5098000168800354},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5016999840736389},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4982999861240387},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.49300000071525574},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.48739999532699585},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.40610000491142273},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.39410001039505005},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3709999918937683},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.3671000003814697},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.33149999380111694},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3296000063419342},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.26460000872612}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/make8010017","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make8010017","pdf_url":"https://www.mdpi.com/2504-4990/8/1/17/pdf?version=1767885893","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:f04a1af071b543399cdb0f7ddb20a128","is_oa":true,"landing_page_url":"https://doaj.org/article/f04a1af071b543399cdb0f7ddb20a128","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning and Knowledge Extraction, Vol 8, Iss 1, p 17 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/make8010017","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make8010017","pdf_url":"https://www.mdpi.com/2504-4990/8/1/17/pdf?version=1767885893","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.6120569705963135,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G1502497843","display_name":null,"funder_award_id":"2023Z073052003","funder_id":"https://openalex.org/F4320327472","funder_display_name":"Chinese Aeronautical Establishment"},{"id":"https://openalex.org/G6217827934","display_name":null,"funder_award_id":"62176122","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320327472","display_name":"Chinese Aeronautical Establishment","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7119530788.pdf","grobid_xml":"https://content.openalex.org/works/W7119530788.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,10,45,105,125,151,162,171,177,210,214,234,237],"rapid":[2],"development":[3],"of":[4,12,107,112,236],"applications":[5],"such":[6],"as":[7],"edge":[8,50,53],"computing,":[9],"Internet":[11],"Things":[13],"(IoT),":[14],"and":[15,35,61,69,85,115,133,168,202,229,247],"embodied":[16],"intelligence,":[17],"massive":[18],"multimodal":[19,108,145,251],"data":[20,72,79,109],"are":[21,55,185],"continuously":[22],"generated":[23],"on":[24,49,95,129,150,197],"end":[25],"devices":[26,54],"in":[27,37,110,226,240,254],"a":[28,130,144],"streaming":[29],"manner.":[30],"To":[31,137],"maintain":[32],"model":[33,126],"adaptability":[34],"robustness":[36],"dynamic":[38],"environments,":[39],"incremental":[40],"learning":[41,255],"has":[42],"gradually":[43],"become":[44],"core":[46],"training":[47,87,189],"paradigm":[48],"devices.":[51],"However,":[52],"constrained":[56],"by":[57],"limited":[58],"computational,":[59],"storage,":[60],"communication":[62],"resources,":[63],"making":[64],"it":[65],"infeasible":[66],"to":[67,82,121,127],"retain":[68],"process":[70],"all":[71],"samples":[73,180],"over":[74],"time.":[75],"This":[76,118],"necessitates":[77],"efficient":[78],"selection":[80,91,147,178,220,253],"strategies":[81],"reduce":[83],"redundancy":[84],"improve":[86],"efficiency.":[88],"Existing":[89],"sample":[90,97,146,167,252],"methods":[92],"primarily":[93],"focus":[94],"overall":[96],"difficulty":[98],"or":[99],"gradient":[100],"contribution,":[101],"but":[102],"they":[103],"overlook":[104],"heterogeneity":[106],"terms":[111],"information":[113],"content":[114],"discriminative":[116],"power.":[117],"often":[119],"leads":[120],"modality":[122,132,163,183,193],"imbalance,":[123],"causing":[124],"over-rely":[128],"single":[131],"suffer":[134],"performance":[135,217],"degradation.":[136],"address":[138],"this":[139,141],"issue,":[140],"paper":[142],"proposes":[143],"strategy":[148,212,239],"based":[149],"Modality":[152],"Balance":[153],"Score":[154],"(MBS).":[155],"The":[156],"method":[157],"computes":[158],"confidence":[159],"scores":[160],"at":[161],"level":[164],"for":[165,250],"each":[166],"further":[169],"quantifies":[170],"contribution":[172],"differences":[173],"across":[174],"modalities.":[175],"In":[176],"process,":[179],"with":[181,207],"balanced":[182],"contributions":[184],"prioritized,":[186],"thereby":[187],"improving":[188],"efficiency":[190],"while":[191],"alleviating":[192],"bias.":[194],"Experiments":[195],"conducted":[196],"two":[198],"benchmark":[199],"datasets,":[200],"CREMA-D":[201],"AVE,":[203],"demonstrate":[204],"that":[205],"compared":[206],"existing":[208],"approaches,":[209],"MBS":[211],"achieves":[213],"most":[215],"stable":[216],"under":[218],"medium-to-high":[219],"ratios":[221],"(0.25\u20130.4),":[222],"yielding":[223],"superior":[224],"results":[225],"both":[227,244],"accuracy":[228],"robustness.":[230],"These":[231],"findings":[232],"validate":[233],"effectiveness":[235],"proposed":[238],"resource-constrained":[241],"scenarios,":[242],"providing":[243],"theoretical":[245],"insights":[246],"practical":[248],"guidance":[249],"tasks.":[256]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2026-01-09T00:00:00"}