{"id":"https://openalex.org/W7131159489","doi":"https://doi.org/10.1109/access.2026.3667092","title":"Sparseness-Optimized Feature Importance for Time Series Classification","display_name":"Sparseness-Optimized Feature Importance for Time Series Classification","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7131159489","doi":"https://doi.org/10.1109/access.2026.3667092"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3667092","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3667092","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3667092","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079498248","display_name":"Isel Grau","orcid":"https://orcid.org/0000-0002-8035-2887"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Isel Grau","raw_affiliation_strings":["Information Systems Group, Eindhoven University of Technology, Eindhoven, The Netherlands"],"raw_orcid":"https://orcid.org/0000-0002-8035-2887","affiliations":[{"raw_affiliation_string":"Information Systems Group, Eindhoven University of Technology, Eindhoven, The Netherlands","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120618810","display_name":"Gonzalo N\u00e1poles","orcid":null},"institutions":[{"id":"https://openalex.org/I193700539","display_name":"Tilburg University","ror":"https://ror.org/04b8v1s79","country_code":"NL","type":"education","lineage":["https://openalex.org/I193700539"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Gonzalo N\u00e1poles","raw_affiliation_strings":["Department of Intelligent Systems, Tilburg University, Tilburg, The Netherlands"],"raw_orcid":"https://orcid.org/0000-0003-1936-3701","affiliations":[{"raw_affiliation_string":"Department of Intelligent Systems, Tilburg University, Tilburg, The Netherlands","institution_ids":["https://openalex.org/I193700539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126658302","display_name":"Agnieszka Jastrzebska","orcid":null},"institutions":[{"id":"https://openalex.org/I108403487","display_name":"Warsaw University of Technology","ror":"https://ror.org/00y0xnp53","country_code":"PL","type":"education","lineage":["https://openalex.org/I108403487"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Agnieszka Jastrzebska","raw_affiliation_strings":["Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland"],"raw_orcid":"https://orcid.org/0000-0001-5361-5787","affiliations":[{"raw_affiliation_string":"Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland","institution_ids":["https://openalex.org/I108403487"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122042098","display_name":"Yamisleydi Salgueiro","orcid":null},"institutions":[{"id":"https://openalex.org/I134695357","display_name":"University of Talca","ror":"https://ror.org/01s4gpq44","country_code":"CL","type":"education","lineage":["https://openalex.org/I134695357"]}],"countries":["CL"],"is_corresponding":false,"raw_author_name":"Yamisleydi Salgueiro","raw_affiliation_strings":["Department of Industrial Engineering, Faculty of Engineering, Universidad de Talca, Campus Curicó, Curicó, Chile"],"raw_orcid":"https://orcid.org/0000-0002-1946-0053","affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, Faculty of Engineering, Universidad de Talca, Campus Curicó, Curicó, Chile","institution_ids":["https://openalex.org/I134695357"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18194993,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"29874","last_page":"29893"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9772999882698059,"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":0.9772999882698059,"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/T10057","display_name":"Face and Expression Recognition","score":0.00139999995008111,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.0013000000035390258,"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/granularity","display_name":"Granularity","score":0.6078000068664551},{"id":"https://openalex.org/keywords/modularity","display_name":"Modularity (biology)","score":0.5608000159263611},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5256999731063843},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5138000249862671},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.4991999864578247},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.4059999883174896},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3846000134944916}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7620999813079834},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.6078000068664551},{"id":"https://openalex.org/C2779478453","wikidata":"https://www.wikidata.org/wiki/Q6889748","display_name":"Modularity (biology)","level":2,"score":0.5608000159263611},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5256999731063843},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5138000249862671},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.4991999864578247},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4943999946117401},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47429999709129333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42730000615119934},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.4059999883174896},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3846000134944916},{"id":"https://openalex.org/C177918212","wikidata":"https://www.wikidata.org/wiki/Q803623","display_name":"Perturbation (astronomy)","level":2,"score":0.3783000111579895},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.37400001287460327},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.3075999915599823},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3028999865055084},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.30230000615119934},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.29809999465942383},{"id":"https://openalex.org/C108154423","wikidata":"https://www.wikidata.org/wiki/Q1469792","display_name":"Salience (neuroscience)","level":2,"score":0.2612999975681305}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/access.2026.3667092","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3667092","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:pure.tue.nl:openaire/09862356-db9b-4ccd-b6cf-a105fec26ed0","is_oa":true,"landing_page_url":"https://research.tue.nl/en/publications/09862356-db9b-4ccd-b6cf-a105fec26ed0","pdf_url":null,"source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Grau, I, N\u00e1poles, G, Jastrzebska, A & Salgueiro, Y 2026, 'Sparseness-Optimized Feature Importance for Time Series Classification', IEEE Access, vol. 14, 11406121, pp. 29874-29893. https://doi.org/10.1109/ACCESS.2026.3667092","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:tilburguniversity.edu:openaire/33de5671-1e1e-4dc3-bb2d-49dbdc499edb","is_oa":true,"landing_page_url":"https://research.tilburguniversity.edu/en/publications/33de5671-1e1e-4dc3-bb2d-49dbdc499edb","pdf_url":null,"source":{"id":"https://openalex.org/S4306401490","display_name":"Research portal (Tilburg University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I193700539","host_organization_name":"Tilburg University","host_organization_lineage":["https://openalex.org/I193700539"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Grau, I, N\u00e1poles, G, Jastrzebska, A & Salgueiro, Y 2026, 'Sparseness-optimized feature importance for time series classification', Ieee access, vol. 14, pp. 29874-29893. https://doi.org/10.1109/ACCESS.2026.3667092","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3667092","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3667092","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.6787284016609192,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[{"id":"https://openalex.org/G1910748309","display_name":"DEEP LEARNING-DRIVEN MULTI-OBJECTIVE EVOLUTIONARY OPTIMIZATION MODELS AND ALGORITHMS FOR RENEWABLE ENERGY SYSTEMS.","funder_award_id":"1240293","funder_id":"https://openalex.org/F4320331146","funder_display_name":"Agencia Nacional de Investigaci\u00f3n y Desarrollo"},{"id":"https://openalex.org/G2011864131","display_name":null,"funder_award_id":"FB210017","funder_id":"https://openalex.org/F4320327261","funder_display_name":"National Center for Research and Development"},{"id":"https://openalex.org/G2670390765","display_name":"ENFIELD: European Lighthouse to Manifest Trustworthy and Green AI","funder_award_id":"101120657","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G3707691175","display_name":null,"funder_award_id":"101120657","funder_id":"https://openalex.org/F4320338453","funder_display_name":"HORIZON EUROPE European Research Council"},{"id":"https://openalex.org/G7877382544","display_name":"NATIONAL CENTER FOR ARTIFICIAL INTELLIGENCE RESEARCH (NAIR)","funder_award_id":"FB210017","funder_id":"https://openalex.org/F4320331146","funder_display_name":"Agencia Nacional de Investigaci\u00f3n y Desarrollo"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"},{"id":"https://openalex.org/F4320327261","display_name":"National Center for Research and Development","ror":null},{"id":"https://openalex.org/F4320331146","display_name":"Agencia Nacional de Investigaci\u00f3n y Desarrollo","ror":null},{"id":"https://openalex.org/F4320338453","display_name":"HORIZON EUROPE European Research Council","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"literature":[1],"reports":[2],"a":[3,118,156],"wide":[4],"variety":[5],"of":[6,67,117,120,203],"attribution":[7],"methods":[8,23,70],"for":[9,52,71,206,247],"explaining":[10],"the":[11,49,57,102,112,115,164,167,177,191,201,215,243],"predictions":[12,103],"made":[13],"by":[14,105,142,242],"time":[15,121,208],"series":[16,122,209],"classification":[17],"(TSC)":[18],"algorithms.":[19],"These":[20],"post-hoc":[21],"explanation":[22,69,113,216],"span":[24],"from":[25],"model-specific":[26],"to":[27,43,55,62,100,128,144,190,230],"agnostic":[28,89],"procedures":[29],"that":[30,75,96,146,162,184,221,225],"operate":[31],"at":[32],"different":[33,204],"granularity":[34],"levels.":[35],"Despite":[36],"their":[37],"relative":[38],"success,":[39],"they":[40,76],"often":[41],"fail":[42],"generate":[44],"sparse":[45],"explanations,":[46],"thus":[47],"increasing":[48],"cognitive":[50],"overhead":[51],"experts":[53,143],"seeking":[54],"isolate":[56],"most":[58],"relevant":[59],"features":[60],"linked":[61],"model":[63,132,193],"performance.":[64,133],"Another":[65],"limitation":[66],"segment-based":[68],"TSC":[72,108],"problems":[73],"is":[74,174,180],"do":[77],"not":[78],"allow":[79],"any":[80,106],"expert":[81],"intervention.":[82],"In":[83,110,196],"this":[84],"paper,":[85],"we":[86,159,182,199],"present":[87],"an":[88],"explainer":[90],"termed":[91],"Sparseness-Optimized":[92],"Feature":[93],"Importance":[94],"(SOFI)":[95],"can":[97],"be":[98,138],"used":[99,246],"explain":[101],"generated":[104,241],"black-box":[107],"model.":[109],"practice,":[111],"takes":[114],"form":[116],"ranking":[119,170],"segments":[123,135,210],"whose":[124],"cumulative":[125],"perturbation":[126,212],"leads":[127],"fast":[129],"degradation":[130],"in":[131],"Those":[134],"should":[136],"ideally":[137],"provided":[139],"or":[140],"defined":[141],"ensure":[145],"explanations":[147,224],"are":[148,226],"meaningful":[149],"and":[150,211,235],"aligned":[151],"with":[152,172],"domain":[153],"knowledge.":[154],"As":[155],"second":[157],"contribution,":[158],"mathematically":[160],"demonstrate":[161],"under":[163],"modularity":[165,178],"assumption,":[166],"optimal":[168,192],"segment":[169,186],"associated":[171],"SOFI":[173,222],"unique.":[175],"If":[176],"assumption":[179],"dropped,":[181],"prove":[183],"multiple":[185],"importance":[187],"rankings":[188],"lead":[189],"performance":[194],"degradation.":[195],"our":[197],"experiments,":[198],"study":[200],"effect":[202],"strategies":[205],"computing":[207],"operators":[213],"on":[214],"results.":[217],"Simulation":[218],"results":[219],"show":[220],"generates":[223],"equally":[227],"robust,":[228],"up":[229],"16":[231],"times":[232,237],"more":[233],"faithful,":[234],"1.4":[236],"sparser":[238],"than":[239],"those":[240],"state-of-the-art":[244],"explainers":[245],"comparison.":[248]},"counts_by_year":[],"updated_date":"2026-03-03T06:13:14.889584","created_date":"2026-02-24T00:00:00"}