{"id":"https://openalex.org/W7138084693","doi":"https://doi.org/10.1609/aaai.v40i29.39682","title":"Beyond Missing Data Imputation: Information-Theoretic Coupling of Missingness and Class Imbalance for Optimal Irregular Time Series Classification","display_name":"Beyond Missing Data Imputation: Information-Theoretic Coupling of Missingness and Class Imbalance for Optimal Irregular Time Series Classification","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138084693","doi":"https://doi.org/10.1609/aaai.v40i29.39682"},"language":null,"primary_location":{"id":"doi:10.1609/aaai.v40i29.39682","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i29.39682","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v40i29.39682","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129672113","display_name":"Xin Qin","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Xin Qin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034243574","display_name":"Mengna Liu","orcid":"https://orcid.org/0009-0000-4729-0592"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mengna Liu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129709438","display_name":"Wenjie Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenjie Wang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107628565","display_name":"Shuxin Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shuxin Li","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129721338","display_name":"Tianjiao Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tianjiao Li","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129688804","display_name":"Xiufeng Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiufeng Liu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129646536","display_name":"Xu Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu Cheng","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5129672113"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.30176565,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"29","first_page":"24945","last_page":"24953"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.42989999055862427,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.42989999055862427,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.4047999978065491,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.017500000074505806,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/missing-data","display_name":"Missing data","score":0.8747000098228455},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6507999897003174},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6175000071525574},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6165000200271606},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5931000113487244},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5884000062942505},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5644999742507935},{"id":"https://openalex.org/keywords/timestamp","display_name":"Timestamp","score":0.5598000288009644},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5117999911308289}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.8747000098228455},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6507999897003174},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6175000071525574},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6165000200271606},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5931000113487244},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5884000062942505},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5673999786376953},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5644999742507935},{"id":"https://openalex.org/C113954288","wikidata":"https://www.wikidata.org/wiki/Q186885","display_name":"Timestamp","level":2,"score":0.5598000288009644},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5117999911308289},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5042999982833862},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4966000020503998},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4523000121116638},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44699999690055847},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.43549999594688416},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4350999891757965},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.40049999952316284},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.35589998960494995},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.35089999437332153},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.34709998965263367},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.34290000796318054},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3334999978542328},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3292999863624573},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3262999951839447},{"id":"https://openalex.org/C200873422","wikidata":"https://www.wikidata.org/wiki/Q5448821","display_name":"Filling-in","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30790001153945923},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.2883000075817108},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28630000352859497},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.267300009727478},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.2522999942302704},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v40i29.39682","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i29.39682","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i29.39682","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i29.39682","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.729462206363678}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Irregular":[0],"time":[1],"series":[2],"(IRTS)":[3],"are":[4],"prevalent":[5],"in":[6,118],"real-world":[7],"applications,":[8],"where":[9],"uneven":[10],"sampling":[11],"and":[12,42,65,78,95,125,141,166,182,192],"missing":[13,139,191],"data":[14,54,140],"pose":[15],"fundamental":[16],"challenges":[17],"to":[18,27,44,80],"deep":[19],"learning-based":[20],"feature":[21,161],"modeling.":[22],"Although":[23],"existing":[24],"methods":[25],"attempt":[26],"retain":[28],"timestamp":[29],"information,":[30],"they":[31],"often":[32],"overlook":[33],"the":[34,39,60,135,156,160,184],"structured":[35],"patterns":[36],"embedded":[37],"within":[38],"missingness":[40,130],"itself,":[41],"tend":[43],"perform":[45],"poorly":[46],"when":[47],"confronted":[48],"with":[49],"class":[50,172],"imbalance":[51],"exacerbated":[52],"by":[53,171],"incompleteness.":[55],"Specifically,":[56],"temporal":[57,116],"irregularity":[58],"hinders":[59],"modeling":[61],"of":[62,138,159,187],"long-range":[63],"dependencies":[64,117],"local":[66],"patterns,":[67],"while":[68],"sparse":[69],"observations":[70],"limit":[71],"representational":[72],"capacity,":[73],"disproportionately":[74],"impairing":[75],"minority":[76],"classes":[77],"leading":[79],"severe":[81],"classification":[82,152],"bias.":[83],"To":[84],"address":[85],"these":[86],"deeply":[87],"coupled":[88],"challenges,":[89],"we":[90],"propose":[91],"SPECTRA":[92,108,188],"(Structured":[93],"Pattern":[94],"Enriched":[96],"Context-aware":[97],"Temporal":[98],"Representation":[99],"Architecture),":[100],"a":[101,110,119,129,145,150],"unified":[102],"framework":[103],"for":[104],"robust":[105],"IRTS":[106,179],"classification.":[107],"introduces":[109],"frequency-guided":[111],"observation":[112],"encoder":[113,132],"that":[114],"reconstructs":[115],"stable":[120],"manner,":[121],"mitigating":[122],"spectral":[123],"distortion":[124],"information":[126],"corruption.":[127],"Complementarily,":[128],"pattern":[131],"explicitly":[133],"captures":[134],"dynamic":[136],"evolution":[137],"leverages":[142],"it":[143],"as":[144],"discriminative":[146],"signal.":[147],"In":[148],"addition,":[149],"prototype-constrained":[151],"paradigm":[153],"directly":[154],"optimizes":[155],"geometric":[157],"structure":[158],"space,":[162],"enhancing":[163],"intra-class":[164],"compactness":[165],"alleviating":[167],"generalization":[168],"bottlenecks":[169],"caused":[170],"imbalance.":[173],"Extensive":[174],"experiments":[175],"on":[176],"three":[177],"public":[178],"datasets\u2014P12,":[180],"P19,":[181],"PAM\u2014demonstrate":[183],"superior":[185],"performance":[186],"under":[189],"both":[190],"imbalanced":[193],"conditions.":[194]},"counts_by_year":[],"updated_date":"2026-03-18T06:31:55.123368","created_date":"2026-03-18T00:00:00"}
