{"id":"https://openalex.org/W2182609147","doi":"https://doi.org/10.1109/icmlc.2015.7340913","title":"Towards learning segmented temporal sequences: A decision tree approach","display_name":"Towards learning segmented temporal sequences: A decision tree approach","publication_year":2015,"publication_date":"2015-07-01","ids":{"openalex":"https://openalex.org/W2182609147","doi":"https://doi.org/10.1109/icmlc.2015.7340913","mag":"2182609147"},"language":"en","primary_location":{"id":"doi:10.1109/icmlc.2015.7340913","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlc.2015.7340913","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 International Conference on Machine Learning and Cybernetics (ICMLC)","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/A5058311447","display_name":"Qianqian Shi","orcid":"https://orcid.org/0000-0002-5266-6731"},"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":"Qianqian Shi","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101683266","display_name":"Ying Zhao","orcid":"https://orcid.org/0000-0002-4872-3633"},"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":"Ying Zhao","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100616088","display_name":"Mingliang Liu","orcid":"https://orcid.org/0000-0001-8491-8115"},"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":"Mingliang Liu","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5058311447"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.2872,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.56343284,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"145","last_page":"150"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9976000189781189,"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":0.9976000189781189,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9821000099182129,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9598000049591064,"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/computer-science","display_name":"Computer science","score":0.8269866704940796},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6194080710411072},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5964852571487427},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5431758761405945},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5370278358459473},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5184592604637146},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5000627040863037},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.4983232021331787},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.48796361684799194},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.4772624671459198},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4356250762939453}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8269866704940796},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6194080710411072},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5964852571487427},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5431758761405945},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5370278358459473},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5184592604637146},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5000627040863037},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.4983232021331787},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.48796361684799194},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.4772624671459198},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4356250762939453},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmlc.2015.7340913","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlc.2015.7340913","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 International Conference on Machine Learning and Cybernetics (ICMLC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.7200000286102295}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1513714509","https://openalex.org/W1607481492","https://openalex.org/W1966280301","https://openalex.org/W1997651539","https://openalex.org/W2056625171","https://openalex.org/W2070493638","https://openalex.org/W2112076978","https://openalex.org/W2115383008","https://openalex.org/W2115891031","https://openalex.org/W2122834776","https://openalex.org/W2125283600","https://openalex.org/W2145211911"],"related_works":["https://openalex.org/W2494523064","https://openalex.org/W2943623134","https://openalex.org/W2588219639","https://openalex.org/W2030292806","https://openalex.org/W2215759665","https://openalex.org/W2960358116","https://openalex.org/W3041172967","https://openalex.org/W2749065928","https://openalex.org/W2147155098","https://openalex.org/W4287727129"],"abstract_inverted_index":{"Time":[0],"has":[1],"been":[2],"included":[3],"in":[4,7,89],"learning":[5],"procedure":[6],"domains":[8],"where":[9,35],"observations":[10],"are":[11],"recorded":[12],"on":[13,110],"a":[14,24,36,41,106],"time":[15],"basis.":[16],"However,":[17],"most":[18],"recent":[19],"works":[20],"take":[21],"input":[22],"as":[23,86],"stream":[25],"of":[26,38,78,82,137],"events":[27,77],"from":[28,74],"the":[29,70,90,100,129,135],"same":[30],"entity.":[31,62],"In":[32],"those":[33],"scenarios":[34],"cohort":[37],"entities":[39],"have":[40],"short":[42],"event":[43],"history,":[44],"applying":[45],"existing":[46],"methods":[47],"will":[48],"lead":[49],"to":[50,98,113,122],"an":[51],"isolated":[52],"or":[53],"inaccurate":[54],"(if":[55],"not":[56,132],"both)":[57],"prediction":[58,115],"model":[59,108],"for":[60,150],"each":[61],"To":[63],"address":[64],"this":[65],"problem,":[66],"our":[67],"work":[68,104],"learns":[69],"segmented":[71],"sequences":[72],"merged":[73],"consecutive":[75],"temporal":[76],"all":[79],"entities.":[80],"Instead":[81],"using":[83],"static":[84],"entropy":[85],"splitting":[87],"metric":[88],"training":[91],"process,":[92],"we":[93],"employ":[94],"feedback-directed":[95],"bottom-up":[96],"approach":[97,131],"build":[99],"decision":[101],"tree.":[102],"This":[103],"adopts":[105],"probabilistic":[107],"based":[109],"Bayes'":[111],"theorem":[112],"enhance":[114],"accuracy.":[116],"Moreover,":[117],"it":[118],"supports":[119],"automatic":[120],"parallelization":[121],"reduce":[123],"overhead.":[124],"Experimental":[125],"results":[126],"demonstrate":[127],"that":[128],"proposed":[130],"only":[133],"improves":[134],"accuracy":[136],"prediction,":[138],"but":[139],"also":[140],"facilitates":[141],"fast":[142],"and":[143],"adaptive":[144],"parameter":[145],"tuning,":[146],"which":[147],"is":[148],"essential":[149],"its":[151],"diversified":[152],"use":[153],"cases.":[154]},"counts_by_year":[{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}