{"id":"https://openalex.org/W4416250022","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228102","title":"Receptive Field Aware Contrastive Learning for Multivariate Time Series Classification","display_name":"Receptive Field Aware Contrastive Learning for Multivariate Time Series Classification","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416250022","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228102"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228102","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228102","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","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":"https://openalex.org/A5101347509","display_name":"Fangru Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fangru Lin","raw_affiliation_strings":["Nanjing University of Science and Technology,School of Computer Science and Engineering,Nanjing,China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology,School of Computer Science and Engineering,Nanjing,China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070895035","display_name":"Yuan Gao","orcid":"https://orcid.org/0000-0002-0628-4484"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Gao","raw_affiliation_strings":["Nanjing University of Science and Technology,School of Computer Science and Engineering,Nanjing,China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology,School of Computer Science and Engineering,Nanjing,China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028386349","display_name":"Yuxin Chen","orcid":"https://orcid.org/0009-0006-0550-7945"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxin Chen","raw_affiliation_strings":["Nanjing University of Science and Technology,School of Computer Science and Engineering,Nanjing,China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology,School of Computer Science and Engineering,Nanjing,China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104280471","display_name":"Jingyi Huo","orcid":null},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingyi Huo","raw_affiliation_strings":["Nanjing University of Science and Technology,School of Computer Science and Engineering,Nanjing,China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology,School of Computer Science and Engineering,Nanjing,China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091286800","display_name":"Yan Hui","orcid":"https://orcid.org/0000-0001-7785-677X"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Yan","raw_affiliation_strings":["Nanjing University of Science and Technology,School of Computer Science and Engineering,Nanjing,China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology,School of Computer Science and Engineering,Nanjing,China","institution_ids":["https://openalex.org/I36399199"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101347509"],"corresponding_institution_ids":["https://openalex.org/I36399199"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.45147746,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9785000085830688,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9785000085830688,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T13702","display_name":"Machine Learning in Healthcare","score":0.0044999998062849045,"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/T13487","display_name":"Statistical and numerical algorithms","score":0.0010000000474974513,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5684000253677368},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5142999887466431},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5108000040054321},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5024999976158142},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4661000072956085},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.45739999413490295},{"id":"https://openalex.org/keywords/scope","display_name":"Scope (computer science)","score":0.45590001344680786},{"id":"https://openalex.org/keywords/receptive-field","display_name":"Receptive field","score":0.44040000438690186},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.399399995803833}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7822999954223633},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5863999724388123},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5684000253677368},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5142999887466431},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5108000040054321},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5024999976158142},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4661000072956085},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.45739999413490295},{"id":"https://openalex.org/C2778012447","wikidata":"https://www.wikidata.org/wiki/Q1034415","display_name":"Scope (computer science)","level":2,"score":0.45590001344680786},{"id":"https://openalex.org/C19071747","wikidata":"https://www.wikidata.org/wiki/Q1755207","display_name":"Receptive field","level":2,"score":0.44040000438690186},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42149999737739563},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.399399995803833},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39010000228881836},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.3646000027656555},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3402999937534332},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.3264999985694885},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.3197000026702881},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.319599986076355},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3179999887943268},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.30489999055862427},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2711000144481659}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228102","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228102","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1977882209","https://openalex.org/W2030863907","https://openalex.org/W2963434542","https://openalex.org/W2997353686","https://openalex.org/W3034369844","https://openalex.org/W3035524453","https://openalex.org/W3134736546","https://openalex.org/W3173748501","https://openalex.org/W3184978184","https://openalex.org/W3190152617","https://openalex.org/W3199148273","https://openalex.org/W4205114161","https://openalex.org/W4206136329","https://openalex.org/W4283805364","https://openalex.org/W4310128778","https://openalex.org/W4367042966","https://openalex.org/W4382317959","https://openalex.org/W4387623852","https://openalex.org/W4387682329","https://openalex.org/W4393159722","https://openalex.org/W4393160493","https://openalex.org/W4394699135","https://openalex.org/W4396535339","https://openalex.org/W4399423533","https://openalex.org/W4400726538","https://openalex.org/W4401567681","https://openalex.org/W4404499965"],"related_works":[],"abstract_inverted_index":{"Multivariate":[0],"Time":[1],"Series":[2],"(MTS)":[3],"classification":[4,190],"is":[5,194],"a":[6,53],"critical":[7],"yet":[8],"challenging":[9],"task":[10,29],"in":[11,44],"the":[12,42,45,57,73,79,108,118,137,145,148,154,169],"field":[13,142],"of":[14,81,139,147,174],"data":[15],"mining.":[16],"Recently,":[17],"Graph":[18],"Neural":[19],"Networks":[20],"(GNNs)":[21],"have":[22],"emerged":[23],"as":[24],"powerful":[25],"tools":[26],"for":[27,102],"this":[28],"due":[30],"to":[31,35,67,76,128,134,144,158],"their":[32],"exceptional":[33],"capability":[34],"capture":[36,68,153],"dependencies":[37],"among":[38],"variables":[39],"(nodes).":[40],"However,":[41],"nodes":[43],"graph":[46],"exhibit":[47],"heterogeneity":[48],"where":[49],"each":[50,132,159],"node":[51,87,133],"plays":[52],"distinct":[54],"role":[55],"within":[56],"network.":[58],"Existing":[59],"GNN-based":[60],"methods":[61],"predominantly":[62],"employ":[63],"fixed-size":[64],"receptive":[65,141],"fields":[66],"spatial":[69,155],"information,":[70],"which":[71],"limits":[72],"models\u2019":[74],"ability":[75],"flexibly":[77],"adjust":[78,136],"scope":[80],"information":[82],"aggregation":[83],"based":[84],"on":[85,187],"individual":[86],"characteristics":[88,146],"and":[89,114,172],"importance.":[90],"To":[91,151],"address":[92],"these":[93],"issues,":[94],"we":[95,106],"propose":[96],"Receptive":[97],"Field-Aware":[98],"Contrastive":[99],"Learning":[100],"(RFACL)":[101],"MTS":[103,189],"classification.":[104],"Specifically,":[105],"design":[107],"Dynamic":[109],"Selection":[110],"Hop":[111],"(DSH)":[112],"block":[113,124],"integrate":[115],"it":[116],"into":[117],"contrastive":[119],"learning":[120],"framework.":[121],"The":[122,192],"DSH":[123,165],"assigns":[125],"different":[126],"weights":[127],"various-hop":[129],"neighborhoods,":[130],"allowing":[131],"adaptively":[135],"size":[138],"its":[140],"according":[143],"input":[149],"data.":[150],"further":[152],"relationships":[156],"corresponding":[157],"time":[160],"segment,":[161],"RFACL":[162],"stacks":[163],"multiple":[164],"blocks":[166],"together,":[167],"enabling":[168],"dynamic":[170],"selection":[171],"integration":[173],"multi-scale":[175],"information.":[176],"Extensive":[177],"experiments":[178],"demonstrate":[179],"that":[180],"our":[181],"proposed":[182],"method":[183],"achieves":[184],"state-of-the-art":[185],"performance":[186],"various":[188],"tasks.":[191],"code":[193],"available":[195],"at":[196],"https://github.com/LinHuai01/RFACL.":[197]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}
