{"id":"https://openalex.org/W4402679998","doi":"https://doi.org/10.1145/3653644.3653661","title":"Feature Analysis for Incomplete Time Series Classification","display_name":"Feature Analysis for Incomplete Time Series Classification","publication_year":2024,"publication_date":"2024-04-26","ids":{"openalex":"https://openalex.org/W4402679998","doi":"https://doi.org/10.1145/3653644.3653661"},"language":"en","primary_location":{"id":"doi:10.1145/3653644.3653661","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3653644.3653661","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 3rd International Conference on Frontiers of Artificial Intelligence and Machine Learning","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/A5064138796","display_name":"Lili Guo","orcid":"https://orcid.org/0000-0002-0470-692X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lili Guo","raw_affiliation_strings":["Tsinghua University, China"],"raw_orcid":"https://orcid.org/0000-0002-0470-692X","affiliations":[{"raw_affiliation_string":"Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100373517","display_name":"Jianmin Wang","orcid":"https://orcid.org/0000-0001-6841-7943"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianmin Wang","raw_affiliation_strings":["Tsinghua University, China"],"raw_orcid":"https://orcid.org/0000-0001-6841-7943","affiliations":[{"raw_affiliation_string":"Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073749322","display_name":"Yu He","orcid":"https://orcid.org/0000-0002-0357-681X"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu He","raw_affiliation_strings":["Chinese Academy of Sciences, China"],"raw_orcid":"https://orcid.org/0000-0002-0357-681X","affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, China","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067915043","display_name":"Lei Song","orcid":"https://orcid.org/0000-0003-1609-2192"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Song","raw_affiliation_strings":["Chinese Academy of Sciences, China"],"raw_orcid":"https://orcid.org/0000-0003-1609-2192","affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, China","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010742054","display_name":"Xin Qin","orcid":"https://orcid.org/0009-0004-5426-0307"},"institutions":[{"id":"https://openalex.org/I136765683","display_name":"Tianjin University of Technology","ror":"https://ror.org/00zbe0w13","country_code":"CN","type":"education","lineage":["https://openalex.org/I136765683"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Qin","raw_affiliation_strings":["Tianjin University of Technology, China"],"raw_orcid":"https://orcid.org/0009-0004-5426-0307","affiliations":[{"raw_affiliation_string":"Tianjin University of Technology, China","institution_ids":["https://openalex.org/I136765683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091339016","display_name":"Xu Cheng","orcid":"https://orcid.org/0000-0002-4724-5748"},"institutions":[{"id":"https://openalex.org/I136765683","display_name":"Tianjin University of Technology","ror":"https://ror.org/00zbe0w13","country_code":"CN","type":"education","lineage":["https://openalex.org/I136765683"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xu Cheng","raw_affiliation_strings":["Tianjin University of Technology, China"],"raw_orcid":"https://orcid.org/0000-0002-4724-5748","affiliations":[{"raw_affiliation_string":"Tianjin University of Technology, China","institution_ids":["https://openalex.org/I136765683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5064138796"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1655092,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"266","last_page":"274"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9934999942779541,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9858999848365784,"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/feature","display_name":"Feature (linguistics)","score":0.6666837334632874},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6578890085220337},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6335703730583191},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6239326596260071},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4519011378288269},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4293662905693054},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42548540234565735},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.27190881967544556},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.08941072225570679}],"concepts":[{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6666837334632874},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6578890085220337},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6335703730583191},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6239326596260071},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4519011378288269},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4293662905693054},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42548540234565735},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27190881967544556},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.08941072225570679},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3653644.3653661","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3653644.3653661","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 3rd International Conference on Frontiers of Artificial Intelligence and Machine Learning","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":21,"referenced_works":["https://openalex.org/W2026297770","https://openalex.org/W2612690371","https://openalex.org/W2803805253","https://openalex.org/W2964010366","https://openalex.org/W2988244882","https://openalex.org/W3043567704","https://openalex.org/W3091113335","https://openalex.org/W3100478528","https://openalex.org/W3116705425","https://openalex.org/W3129938072","https://openalex.org/W3189124218","https://openalex.org/W3193597430","https://openalex.org/W3205978767","https://openalex.org/W4212774754","https://openalex.org/W4286906185","https://openalex.org/W4287556785","https://openalex.org/W4289533833","https://openalex.org/W4312181471","https://openalex.org/W4367057034","https://openalex.org/W6739901393","https://openalex.org/W6768707575"],"related_works":["https://openalex.org/W1919101720","https://openalex.org/W4390822878","https://openalex.org/W96888382","https://openalex.org/W2041308758","https://openalex.org/W4386126592","https://openalex.org/W4392529072","https://openalex.org/W4386159726","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"This":[0],"study":[1],"introduces":[2],"an":[3],"innovative":[4],"imputation-free":[5,87],"framework":[6,67],"for":[7,86],"time":[8,52,58],"series":[9,53,59],"classification":[10],"amidst":[11],"missing":[12,63,81],"values,":[13],"challenging":[14],"traditional":[15],"imputation-based":[16],"approaches.":[17],"By":[18],"integrating":[19],"GRU-based":[20],"imputation":[21],"with":[22],"a":[23,76],"noisy-robust":[24],"feature":[25],"learning":[26],"module,":[27],"our":[28],"model":[29],"adeptly":[30],"navigates":[31],"the":[32,43,65,84],"complexities":[33],"of":[34,46],"incomplete":[35],"data,":[36,82],"reducing":[37],"reliance":[38],"on":[39],"assumptions":[40],"and":[41,55,93],"harnessing":[42],"informational":[44],"essence":[45],"missingness.":[47],"Tested":[48],"across":[49],"20":[50],"univariate":[51],"datasets":[54],"one":[56],"multivariate":[57],"data":[60],"under":[61],"various":[62],"ratios,":[64],"proposed":[66],"not":[68],"only":[69],"outperforms":[70],"conventional":[71],"methods":[72],"but":[73],"also":[74],"marks":[75],"paradigm":[77],"shift":[78],"in":[79,95],"handling":[80],"illustrating":[83],"potential":[85],"strategies":[88],"to":[89],"enhance":[90],"analysis":[91],"accuracy":[92],"reliability":[94],"critical":[96],"data-intensive":[97],"applications.":[98]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
