{"id":"https://openalex.org/W3193366935","doi":"https://doi.org/10.1007/s10618-021-00781-5","title":"MultiETSC: automated machine learning for early time series classification","display_name":"MultiETSC: automated machine learning for early time series classification","publication_year":2021,"publication_date":"2021-08-16","ids":{"openalex":"https://openalex.org/W3193366935","doi":"https://doi.org/10.1007/s10618-021-00781-5","mag":"3193366935"},"language":"en","primary_location":{"id":"doi:10.1007/s10618-021-00781-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-021-00781-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-021-00781-5.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10618-021-00781-5.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5057578459","display_name":"Gilles Ottervanger","orcid":"https://orcid.org/0000-0002-0979-9379"},"institutions":[{"id":"https://openalex.org/I121797337","display_name":"Leiden University","ror":"https://ror.org/027bh9e22","country_code":"NL","type":"education","lineage":["https://openalex.org/I121797337"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Gilles Ottervanger","raw_affiliation_strings":["Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands"],"affiliations":[{"raw_affiliation_string":"Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands","institution_ids":["https://openalex.org/I121797337"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038925727","display_name":"Mitra Baratchi","orcid":"https://orcid.org/0000-0002-1279-9310"},"institutions":[{"id":"https://openalex.org/I121797337","display_name":"Leiden University","ror":"https://ror.org/027bh9e22","country_code":"NL","type":"education","lineage":["https://openalex.org/I121797337"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Mitra Baratchi","raw_affiliation_strings":["Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands"],"affiliations":[{"raw_affiliation_string":"Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands","institution_ids":["https://openalex.org/I121797337"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025342513","display_name":"Holger H. Hoos","orcid":"https://orcid.org/0000-0003-0629-0099"},"institutions":[{"id":"https://openalex.org/I121797337","display_name":"Leiden University","ror":"https://ror.org/027bh9e22","country_code":"NL","type":"education","lineage":["https://openalex.org/I121797337"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Holger H. Hoos","raw_affiliation_strings":["Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands"],"affiliations":[{"raw_affiliation_string":"Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands","institution_ids":["https://openalex.org/I121797337"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5057578459"],"corresponding_institution_ids":["https://openalex.org/I121797337"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":0.7695,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.71042212,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"35","issue":"6","first_page":"2602","last_page":"2654"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9991999864578247,"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":0.9991999864578247,"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/T11309","display_name":"Music and Audio Processing","score":0.9258000254631042,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9218000173568726,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.8996585607528687},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.724370539188385},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7022320032119751},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6899261474609375},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"Hyperparameter optimization","score":0.6283174157142639},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5911750197410583},{"id":"https://openalex.org/keywords/pareto-principle","display_name":"Pareto principle","score":0.4381408989429474},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4334600865840912},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3805058002471924},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3220498263835907},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.24469619989395142},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.15932774543762207},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14773136377334595}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.8996585607528687},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.724370539188385},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7022320032119751},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6899261474609375},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.6283174157142639},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5911750197410583},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.4381408989429474},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4334600865840912},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3805058002471924},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3220498263835907},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.24469619989395142},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.15932774543762207},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14773136377334595},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1007/s10618-021-00781-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-021-00781-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-021-00781-5.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},{"id":"pmh:oai:scholarlypublications.universiteitleiden.nl:item_3275390","is_oa":true,"landing_page_url":"https://hdl.handle.net/1887/3275390","pdf_url":null,"source":{"id":"https://openalex.org/S4306400850","display_name":"Leiden Repository (Leiden University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I121797337","host_organization_name":"Leiden University","host_organization_lineage":["https://openalex.org/I121797337"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Data Mining and Knowledge Discovery","raw_type":"Article / Letter to editor"},{"id":"pmh:ul:oai:scholarlypublications.universiteitleiden.nl:item_3275390","is_oa":true,"landing_page_url":"http://hdl.handle.net/1887/3275390","pdf_url":null,"source":{"id":"https://openalex.org/S4306401843","display_name":"Data Archiving and Networked Services (DANS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1322597698","host_organization_name":"Royal Netherlands Academy of Arts and Sciences","host_organization_lineage":["https://openalex.org/I1322597698"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Data Mining and Knowledge Discovery, 35, 2602\u20132654. SPRINGER","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1007/s10618-021-00781-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-021-00781-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-021-00781-5.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals","score":0.41999998688697815}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3193366935.pdf","grobid_xml":"https://content.openalex.org/works/W3193366935.grobid-xml"},"referenced_works_count":46,"referenced_works":["https://openalex.org/W14817117","https://openalex.org/W60686164","https://openalex.org/W114730584","https://openalex.org/W1540371141","https://openalex.org/W1565746575","https://openalex.org/W1946505397","https://openalex.org/W2039459330","https://openalex.org/W2078765398","https://openalex.org/W2097998348","https://openalex.org/W2098899567","https://openalex.org/W2102539288","https://openalex.org/W2106411961","https://openalex.org/W2113955139","https://openalex.org/W2125899728","https://openalex.org/W2182361439","https://openalex.org/W2309832917","https://openalex.org/W2342546612","https://openalex.org/W2403962807","https://openalex.org/W2432472326","https://openalex.org/W2468065612","https://openalex.org/W2548304608","https://openalex.org/W2555077524","https://openalex.org/W2564745357","https://openalex.org/W2581867724","https://openalex.org/W2588875990","https://openalex.org/W2765566334","https://openalex.org/W2775212671","https://openalex.org/W2798650501","https://openalex.org/W2895667350","https://openalex.org/W2902760701","https://openalex.org/W2911439933","https://openalex.org/W2938764606","https://openalex.org/W2940205355","https://openalex.org/W2950680102","https://openalex.org/W2951920403","https://openalex.org/W2963798004","https://openalex.org/W2964024268","https://openalex.org/W2970971581","https://openalex.org/W2988244882","https://openalex.org/W3003076319","https://openalex.org/W3015626561","https://openalex.org/W3036081019","https://openalex.org/W3098918569","https://openalex.org/W3100008240","https://openalex.org/W3105444733","https://openalex.org/W6600339457"],"related_works":["https://openalex.org/W2953665647","https://openalex.org/W4205712847","https://openalex.org/W4281646320","https://openalex.org/W3169687406","https://openalex.org/W2954882791","https://openalex.org/W4287818966","https://openalex.org/W4388119537","https://openalex.org/W3014750173","https://openalex.org/W3192751261","https://openalex.org/W3200811867"],"abstract_inverted_index":{"Abstract":[0],"Early":[1],"time":[2,20,155],"series":[3,156],"classification":[4,212],"(EarlyTSC)":[5],"involves":[6],"the":[7,26,67,75,93,104,132,145],"prediction":[8],"of":[9,17,51,106,148,187,240,247],"a":[10,18,37,49,101,161,185,194,198,229,245],"class":[11],"label":[12],"based":[13],"on":[14,129,222,244],"partial":[15],"observation":[16],"given":[19],"series.":[21],"Most":[22],"EarlyTSC":[23,107,181,218],"algorithms":[24,213],"consider":[25],"trade-off":[27,46,68],"between":[28],"accuracy":[29],"and":[30,82,99,123,167,183,209,272],"earliness":[31],"as":[32,137],"two":[33,150],"competing":[34],"objectives,":[35],"using":[36,228],"single":[38],"dedicated":[39],"hyperparameter.":[40],"To":[41,111,142],"obtain":[42],"insights":[43],"into":[44],"this":[45,58,226],"requires":[47],"finding":[48],"set":[50,186],"non-dominated":[52],"(Pareto":[53],"efficient)":[54],"classifiers.":[55],"So":[56],"far,":[57],"has":[59],"been":[60],"approached":[61],"through":[62],"manual":[63,78],"hyperparameter":[64,89,97,121,168],"tuning.":[65],"Since":[66],"hyperparameters":[69],"only":[70],"provide":[71],"indirect":[72],"control":[73],"over":[74],"earliness-accuracy":[76],"trade-off,":[77],"tuning":[79,122],"is":[80],"tedious":[81],"tends":[83],"to":[84,108,120,267],"result":[85],"in":[86,131,153],"many":[87],"sub-optimal":[88],"settings.":[90],"This":[91],"complicates":[92],"search":[94],"for":[95,103,126,163,171,217,219],"optimal":[96,189],"settings":[98],"forms":[100],"hurdle":[102],"application":[105],"real-world":[109],"problems.":[110],"address":[112],"these":[113],"issues,":[114],"we":[115,158,224,251],"propose":[116,159],"an":[117,201,236],"automated":[118,138],"approach":[119,243],"algorithm":[124,165,182,190],"selection":[125,166],"EarlyTSC,":[127],"building":[128],"developments":[130],"fast-moving":[133],"research":[134],"area":[135],"known":[136],"machine":[139],"learning":[140],"(AutoML).":[141],"deal":[143],"with":[144],"challenging":[146],"task":[147],"optimising":[149],"conflicting":[151],"objectives":[152],"early":[154],"classification,":[157],"MultiETSC,":[160],"system":[162],"multi-objective":[164],"optimisation":[169],"(MO-CASH)":[170],"EarlyTSC.":[172],"MultiETSC":[173,254],"can":[174,196,207],"potentially":[175],"leverage":[176,210],"any":[177],"existing":[178],"or":[179],"future":[180],"produces":[184],"Pareto":[188],"configurations":[191],"from":[192],"which":[193],"user":[195],"choose":[197],"posteriori.":[199],"As":[200],"additional":[202],"benefit,":[203],"our":[204,241],"proposed":[205],"framework":[206],"incorporate":[208],"time-series":[211],"not":[214],"originally":[215],"designed":[216],"improving":[220],"performance":[221],"EarlyTSC;":[223],"demonstrate":[225],"property":[227],"newly":[230],"defined,":[231],"\u201cna\u00efve\u201d":[232],"fixed-time":[233],"algorithm.":[234],"In":[235],"extensive":[237],"empirical":[238],"evaluation":[239],"new":[242],"benchmark":[246],"115":[248],"data":[249],"sets,":[250],"show":[252],"that":[253],"performs":[255],"substantially":[256],"better":[257],"than":[258],"baseline":[259],"methods,":[260],"ranking":[261],"highest":[262],"(avg.":[263],"rank":[264],"1.98)":[265],"compared":[266],"conceptually":[268],"simpler":[269],"single-algorithm":[270],"(2.98)":[271],"single-objective":[273],"alternatives":[274],"(4.36).":[275]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
