{"id":"https://openalex.org/W4392904541","doi":"https://doi.org/10.1109/icassp48485.2024.10447536","title":"Treemil: A Multi-Instance Learning Framework for Time Series Anomaly Detection with Inexact Supervision","display_name":"Treemil: A Multi-Instance Learning Framework for Time Series Anomaly Detection with Inexact Supervision","publication_year":2024,"publication_date":"2024-03-18","ids":{"openalex":"https://openalex.org/W4392904541","doi":"https://doi.org/10.1109/icassp48485.2024.10447536"},"language":"en","primary_location":{"id":"doi:10.1109/icassp48485.2024.10447536","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10447536","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5100322175","display_name":"Chen Liu","orcid":"https://orcid.org/0000-0002-8009-7978"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chen Liu","raw_affiliation_strings":["Zhejiang University"],"affiliations":[{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068195118","display_name":"Shibo He","orcid":"https://orcid.org/0000-0002-1505-6766"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shibo He","raw_affiliation_strings":["Zhejiang University"],"affiliations":[{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067320802","display_name":"Haoyu Liu","orcid":"https://orcid.org/0000-0002-8998-1217"},"institutions":[{"id":"https://openalex.org/I4210091137","display_name":"NetEase (China)","ror":"https://ror.org/00fp6fj05","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210091137"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoyu Liu","raw_affiliation_strings":["NetEase Fuxi AI Lab"],"affiliations":[{"raw_affiliation_string":"NetEase Fuxi AI Lab","institution_ids":["https://openalex.org/I4210091137"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102015526","display_name":"Shizhong Li","orcid":"https://orcid.org/0000-0003-4429-3549"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shizhong Li","raw_affiliation_strings":["Zhejiang University"],"affiliations":[{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100322175"],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":2.5383,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.90140327,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"7510","last_page":"7514"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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":0.9991000294685364,"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/T11819","display_name":"Data-Driven Disease Surveillance","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7388923168182373},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7081285715103149},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6813682317733765},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.6056375503540039},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.519830048084259},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.47737857699394226},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4610513746738434},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4280211627483368},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4216495156288147},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.42015740275382996},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4129208028316498},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10553288459777832}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7388923168182373},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7081285715103149},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6813682317733765},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.6056375503540039},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.519830048084259},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.47737857699394226},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4610513746738434},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4280211627483368},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4216495156288147},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.42015740275382996},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4129208028316498},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10553288459777832},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp48485.2024.10447536","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10447536","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6700000166893005,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2792571661","https://openalex.org/W2947464817","https://openalex.org/W2963795951","https://openalex.org/W2965433388","https://openalex.org/W3169450514","https://openalex.org/W3177318507","https://openalex.org/W3193785691","https://openalex.org/W3204263062","https://openalex.org/W4221150214","https://openalex.org/W4283811196","https://openalex.org/W4309765030","https://openalex.org/W4312443721","https://openalex.org/W4372341460","https://openalex.org/W4382461973","https://openalex.org/W4385245566","https://openalex.org/W4385568255","https://openalex.org/W4386076087","https://openalex.org/W4386076149","https://openalex.org/W6751494907","https://openalex.org/W6763203961","https://openalex.org/W6802061597","https://openalex.org/W6810225340"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2667207928","https://openalex.org/W2912112202","https://openalex.org/W4377864969","https://openalex.org/W2972971679"],"abstract_inverted_index":{"Time":[0],"series":[1,75,125],"anomaly":[2,67,156],"detection":[3,23],"(TSAD)":[4],"plays":[5],"a":[6,56,109],"vital":[7],"role":[8],"in":[9,104,183],"various":[10],"domains":[11],"such":[12],"as":[13],"healthcare,":[14],"networks":[15],"and":[16,172],"industry.":[17],"Considering":[18],"labels":[19,37],"are":[20,38,47,77,143],"crucial":[21],"for":[22],"but":[24],"difficult":[25],"to":[26,30,81,121,145,186],"obtain,":[27],"we":[28,107,153],"turn":[29],"TSAD":[31],"with":[32,136],"inexact":[33],"supervision:":[34],"only":[35,79],"series-level":[36],"provided":[39],"during":[40,49],"the":[41,50,99,123,140,147],"training":[42],"phase,":[43],"while":[44],"point-level":[45,155],"anomalies":[46,76],"predicted":[48],"testing":[51],"phase.":[52],"Previous":[53],"works":[54],"follow":[55],"traditional":[57],"multi-instance":[58],"learning":[59],"(MIL)":[60],"approach,":[61],"which":[62],"focuses":[63],"on":[64,168],"encouraging":[65],"high":[66],"scores":[68,157],"at":[69,131,163,194],"individual":[70,82],"time":[71,74],"steps.":[72],"However,":[73],"not":[78],"limited":[80],"point":[83],"anomalies,":[84,90,103],"they":[85],"can":[86],"also":[87],"be":[88],"collective":[89,102,150],"typically":[91],"exhibiting":[92],"abnormal":[93],"patterns":[94],"over":[95],"subsequences.":[96],"To":[97],"address":[98],"challenge":[100],"of":[101,149],"this":[105],"paper,":[106],"propose":[108],"tree-based":[110],"MIL":[111],"framework":[112],"(TreeMIL).":[113],"We":[114],"first":[115],"adopt":[116],"an":[117,179],"N-ary":[118],"tree":[119],"structure":[120],"divide":[122],"entire":[124],"into":[126],"multiple":[127],"nodes,":[128],"where":[129],"nodes":[130,162],"different":[132,137,164],"levels":[133],"represent":[134],"subsequences":[135],"lengths.":[138],"Then,":[139],"subsequences\u2019":[141],"features":[142,160],"extracted":[144],"determine":[146],"presence":[148],"anomalies.":[151],"Finally,":[152],"calculate":[154],"by":[158],"aggregating":[159],"from":[161],"levels.":[165],"Experiments":[166],"conducted":[167],"seven":[169],"public":[170],"datasets":[171],"eight":[173],"baselines":[174],"demonstrate":[175],"that":[176],"TreeMIL":[177],"achieves":[178],"average":[180],"32.3%":[181],"improvement":[182],"F1-score":[184],"compared":[185],"previous":[187],"state-of-the-art":[188],"methods.":[189],"The":[190],"code":[191],"is":[192],"available":[193],"https://github.com/fly-orange/TreeMIL.":[195]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
