{"id":"https://openalex.org/W3010735150","doi":"https://doi.org/10.1109/sips47522.2019.9020321","title":"Feature Selection Framework for XGBoost Based on Electrodermal Activity in Stress Detection","display_name":"Feature Selection Framework for XGBoost Based on Electrodermal Activity in Stress Detection","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W3010735150","doi":"https://doi.org/10.1109/sips47522.2019.9020321","mag":"3010735150"},"language":"en","primary_location":{"id":"doi:10.1109/sips47522.2019.9020321","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sips47522.2019.9020321","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","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/A5073933434","display_name":"Cheng-Ping Hsieh","orcid":"https://orcid.org/0009-0004-5795-9113"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Cheng-Ping Hsieh","raw_affiliation_strings":["Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan","National Taiwan University, Department of Electrical Engineering, Taipei, Taiwan#TAB#"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan","institution_ids":["https://openalex.org/I16733864"]},{"raw_affiliation_string":"National Taiwan University, Department of Electrical Engineering, Taipei, Taiwan#TAB#","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060083525","display_name":"Yi-Ta Chen","orcid":"https://orcid.org/0000-0003-1724-0206"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yi-Ta Chen","raw_affiliation_strings":["Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan","Graduate Institute of Electronics Engineering, National Taiwan University Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan","institution_ids":["https://openalex.org/I16733864"]},{"raw_affiliation_string":"Graduate Institute of Electronics Engineering, National Taiwan University Taipei, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035263226","display_name":"Win-Ken Beh","orcid":"https://orcid.org/0000-0002-3574-8911"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Win-Ken Beh","raw_affiliation_strings":["Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan","Graduate Institute of Electronics Engineering, National Taiwan University Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan","institution_ids":["https://openalex.org/I16733864"]},{"raw_affiliation_string":"Graduate Institute of Electronics Engineering, National Taiwan University Taipei, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109465340","display_name":"An-Yeu Wu","orcid":"https://orcid.org/0000-0003-4731-8633"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"An-Yeu Andy Wu","raw_affiliation_strings":["Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan","Graduate Institute of Electronics Engineering, National Taiwan University Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan","institution_ids":["https://openalex.org/I16733864"]},{"raw_affiliation_string":"Graduate Institute of Electronics Engineering, National Taiwan University Taipei, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5073933434"],"corresponding_institution_ids":["https://openalex.org/I16733864"],"apc_list":null,"apc_paid":null,"fwci":4.4111,"has_fulltext":false,"cited_by_count":55,"citation_normalized_percentile":{"value":0.94610384,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"330","last_page":"335"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9914000034332275,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.9878000020980835,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.6803908348083496},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6721217632293701},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6607317924499512},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6019580364227295},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6013057231903076},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5547931790351868},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5241546630859375},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5122191309928894},{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.4797876477241516},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.4343113303184509},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4189912676811218},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3610461354255676},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15870866179466248}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6803908348083496},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6721217632293701},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6607317924499512},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6019580364227295},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6013057231903076},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5547931790351868},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5241546630859375},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5122191309928894},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.4797876477241516},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.4343113303184509},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4189912676811218},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3610461354255676},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15870866179466248},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sips47522.2019.9020321","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sips47522.2019.9020321","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1567784974","https://openalex.org/W2113555622","https://openalex.org/W2117866167","https://openalex.org/W2136466671","https://openalex.org/W2144279833","https://openalex.org/W2144961120","https://openalex.org/W2164368909","https://openalex.org/W2171801645","https://openalex.org/W2294683276","https://openalex.org/W2295598076","https://openalex.org/W2329439512","https://openalex.org/W2520289546","https://openalex.org/W2537336814","https://openalex.org/W2563752936","https://openalex.org/W2760945358","https://openalex.org/W2888582661","https://openalex.org/W2889963422","https://openalex.org/W2894771803","https://openalex.org/W2963886099","https://openalex.org/W4233733072","https://openalex.org/W4294184534"],"related_works":["https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W1549363203","https://openalex.org/W4231274751","https://openalex.org/W2154063878","https://openalex.org/W2556012038","https://openalex.org/W1489772951","https://openalex.org/W1518215897","https://openalex.org/W1566995892","https://openalex.org/W2145188897"],"abstract_inverted_index":{"Since":[0],"stress":[1,15,35,151],"has":[2],"a":[3],"strong":[4],"influence":[5],"on":[6,38,120],"human's":[7],"health,":[8],"it":[9],"is":[10],"necessary":[11],"to":[12,25,43,57,83,118],"automatically":[13],"detect":[14],"in":[16,34,46,86,105,150],"our":[17],"daily":[18],"life.":[19],"In":[20],"this":[21],"paper,":[22],"we":[23,53,78,107,128],"aim":[24],"improve":[26],"the":[27,31,44,132,138,146],"performance":[28],"and":[29,49,70,88,113,137],"obtain":[30],"dominant":[32,80,103],"features":[33,81,92,104,127],"detection":[36],"based":[37],"Electrodermal":[39],"Activity":[40],"(EDA).":[41],"Compared":[42],"methods":[45],"Wearable":[47],"Stress":[48],"Affect":[50],"Dataset":[51],"(WESAD),":[52],"propose":[54],"several":[55],"enhancements":[56],"get":[58],"higher":[59],"f1-scores,":[60],"including":[61],"less":[62],"overlapped":[63],"signal":[64,67,124,144],"segmentation,":[65],"more":[66],"processing":[68],"features,":[69],"extreme":[71],"gradient":[72],"boosting":[73],"classification":[74],"algorithm":[75],"(XGBoost).":[76],"Furthermore,":[77],"select":[79],"according":[82],"their":[84],"importance":[85],"classifier":[87],"correlation":[89],"among":[90],"other":[91],"while":[93],"keeping":[94],"high":[95,141],"performance.":[96],"Experiment":[97],"results":[98],"show":[99],"that":[100,131],"with":[101],"9":[102],"XGBoost,":[106],"can":[108],"achieve":[109],"92.38%":[110],"(+":[111],"17.87%)":[112],"89.92%":[114],"(+14.58%)":[115],"f1-scores":[116],"compared":[117],"WESAD":[119],"chest-and":[121],"wrist-based":[122],"EDA":[123,143],"respectively.":[125],"The":[126],"choose":[129],"suggest":[130],"magnitude":[133],"of":[134,140],"low":[135],"frequency":[136,142],"complexity":[139],"contain":[145],"most":[147],"significant":[148],"information":[149],"detection.":[152]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}