{"id":"https://openalex.org/W4206640812","doi":"https://doi.org/10.1007/978-3-030-91445-5_3","title":"Fast Channel Selection for Scalable Multivariate Time Series Classification","display_name":"Fast Channel Selection for Scalable Multivariate Time Series Classification","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W4206640812","doi":"https://doi.org/10.1007/978-3-030-91445-5_3"},"language":"en","primary_location":{"id":"doi:10.1007/978-3-030-91445-5_3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-030-91445-5_3","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"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":"book series"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1007/978-3-030-91445-5_3","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010565726","display_name":"Bhaskar Dhariyal","orcid":"https://orcid.org/0009-0000-0218-4825"},"institutions":[{"id":"https://openalex.org/I100930933","display_name":"University College Dublin","ror":"https://ror.org/05m7pjf47","country_code":"IE","type":"education","lineage":["https://openalex.org/I100930933"]}],"countries":["IE"],"is_corresponding":true,"raw_author_name":"Bhaskar Dhariyal","raw_affiliation_strings":["School of Computer Science, University College Dublin, Dublin, Ireland"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, University College Dublin, Dublin, Ireland","institution_ids":["https://openalex.org/I100930933"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040176369","display_name":"Thach Le Nguyen","orcid":"https://orcid.org/0000-0002-4532-0548"},"institutions":[{"id":"https://openalex.org/I100930933","display_name":"University College Dublin","ror":"https://ror.org/05m7pjf47","country_code":"IE","type":"education","lineage":["https://openalex.org/I100930933"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Thach Le Nguyen","raw_affiliation_strings":["School of Computer Science, University College Dublin, Dublin, Ireland"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, University College Dublin, Dublin, Ireland","institution_ids":["https://openalex.org/I100930933"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054845773","display_name":"Georgiana Ifrim","orcid":"https://orcid.org/0000-0002-8400-2972"},"institutions":[{"id":"https://openalex.org/I100930933","display_name":"University College Dublin","ror":"https://ror.org/05m7pjf47","country_code":"IE","type":"education","lineage":["https://openalex.org/I100930933"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Georgiana Ifrim","raw_affiliation_strings":["School of Computer Science, University College Dublin, Dublin, Ireland"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, University College Dublin, Dublin, Ireland","institution_ids":["https://openalex.org/I100930933"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5010565726"],"corresponding_institution_ids":["https://openalex.org/I100930933"],"apc_list":{"value":5000,"currency":"EUR","value_usd":5392},"apc_paid":{"value":5000,"currency":"EUR","value_usd":5392},"fwci":2.7258,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.92839259,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"36","last_page":"54"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998999834060669,"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":0.9998999834060669,"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":0.988099992275238,"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/T11309","display_name":"Music and Audio Processing","score":0.9807999730110168,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7192051410675049},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.664724588394165},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5941776037216187},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5483102798461914},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5482726693153381},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5432450175285339},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5085264444351196},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4808250665664673},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.44631797075271606},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4257954955101013},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4243772625923157},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3437092900276184},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07567355036735535}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7192051410675049},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.664724588394165},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5941776037216187},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5483102798461914},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5482726693153381},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5432450175285339},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5085264444351196},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4808250665664673},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.44631797075271606},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4257954955101013},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4243772625923157},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3437092900276184},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07567355036735535},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/978-3-030-91445-5_3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-030-91445-5_3","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"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":"book series"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"}],"best_oa_location":{"id":"doi:10.1007/978-3-030-91445-5_3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-030-91445-5_3","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"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":"book series"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1981456624","https://openalex.org/W2016944175","https://openalex.org/W2097749765","https://openalex.org/W2101591109","https://openalex.org/W2153685828","https://openalex.org/W2164274563","https://openalex.org/W2395121474","https://openalex.org/W2555077524","https://openalex.org/W2946507061","https://openalex.org/W2962730651","https://openalex.org/W2982438846","https://openalex.org/W3042807565","https://openalex.org/W3112538083","https://openalex.org/W3115948762","https://openalex.org/W3132154052","https://openalex.org/W3152879062","https://openalex.org/W4226308683","https://openalex.org/W6600336938"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W972276598","https://openalex.org/W2086519370","https://openalex.org/W2028665553","https://openalex.org/W2087343574","https://openalex.org/W2535915176","https://openalex.org/W2105860728","https://openalex.org/W4287657826"],"abstract_inverted_index":{"Multivariate":[0,56],"time":[1,25],"series":[2,97],"record":[3],"sequences":[4],"of":[5,34,47,88,94,101,115,167,222,231,271,283,311],"values":[6],"using":[7,213,307],"multiple":[8,43],"sensors":[9,44],"or":[10],"channels.":[11],"In":[12,181,257],"the":[13,32,86,113,127,147,153,163,168,220,227,245,260,264,272,275,281,304,312],"classification":[14,296],"task,":[15,149],"we":[16,184,301],"have":[17],"a":[18,29,35,119,141,186,195,232,289,293],"class":[19,210],"label":[20],"associated":[21],"with":[22,66,112,160],"each":[23],"multivariate":[24],"series.":[26],"For":[27],"example,":[28],"smartwatch":[30],"captures":[31],"activity":[33],"person":[36],"over":[37],"time,":[38],"and":[39,69,75,99,117,150,165,179,193,229,237,249,298],"there":[40],"are":[41,144],"typically":[42],"capturing":[45],"aspects":[46],"motion":[48,295],"such":[49],"as":[50,170,172],"acceleration,":[51],"orientation,":[52],"heart":[53],"beat.":[54],"Existing":[55,105],"Time":[57],"Series":[58],"Classification":[59],"(MTSC)":[60],"algorithms":[61,236],"do":[62,108],"not":[63,109],"scale":[64,110],"well":[65,111,171],"large":[67],"datasets,":[68],"this":[70,182],"leads":[71],"to":[72,82,207,268],"extensive":[73],"training":[74,125,157,263],"prediction":[76],"times.":[77],"This":[78],"problem":[79],"is":[80,206],"attributed":[81],"an":[83],"increase":[84],"in":[85,174,288],"number":[87,100,114],"records":[89],"(e.g.,":[90,103],"study":[91,291],"participants),":[92],"duration":[93],"recording":[95],"(time":[96],"length),":[98],"channels":[102,143,155],"sensors).":[104],"MTSC":[106,131,192,235],"methods":[107,121],"channels,":[116],"only":[118,140,308],"few":[120,142,187,233],"can":[122,242,302],"complete":[123],"their":[124],"on":[126,216,226,274,292],"medium":[128],"sized":[129],"UEA":[130],"benchmark":[132],"within":[133],"7":[134],"days.":[135],"Additionally,":[136],"for":[137,146,176,191,198,262],"some":[138,258],"problems,":[139],"relevant":[145,154],"learning":[148],"thus":[151,250],"identifying":[152],"before":[156],"may":[158],"help":[159],"improving":[161],"both":[162],"scalability":[164,230],"accuracy":[166,228,306],"classifiers,":[169],"result":[173],"savings":[175],"data":[177,247],"collection":[178],"storage.":[180],"work,":[183],"investigate":[185],"channel":[188,201,285],"selection":[189,286],"strategies":[190],"propose":[194],"new":[196,224],"approach":[197,241],"fast":[199,214],"supervised":[200],"selection.":[202],"The":[203],"key":[204],"idea":[205],"use":[208],"channel-wise":[209],"separation":[211],"estimation":[212],"computation":[215],"centroid-pairs.":[217],"We":[218,278],"evaluate":[219],"impact":[221],"our":[223,240,284],"method":[225,287],"state-of-the-art":[234],"show":[238,299],"that":[239,300],"dramatically":[243],"reduce":[244],"input":[246],"size,":[248],"improve":[251],"scalability,":[252],"while":[253],"also":[254,279],"preserving":[255],"accuracy.":[256],"cases,":[259],"runtime":[261,273],"classifier":[265],"was":[266],"reduced":[267],"one":[269,309],"third":[270,310],"original":[276],"dataset.":[277],"analyse":[280],"performance":[282],"case":[290],"human":[294],"task":[297],"achieve":[303],"same":[305],"data.":[313]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
