{"id":"https://openalex.org/W3203798346","doi":"https://doi.org/10.1145/3460418.3479375","title":"Triple-O for SHL Recognition Challenge: An Ensemble Framework for Multi-class Imbalance and Training-testing Distribution Inconsistency by OvO Binarization with Confidence Weight of One-class Classification","display_name":"Triple-O for SHL Recognition Challenge: An Ensemble Framework for Multi-class Imbalance and Training-testing Distribution Inconsistency by OvO Binarization with Confidence Weight of One-class Classification","publication_year":2021,"publication_date":"2021-09-21","ids":{"openalex":"https://openalex.org/W3203798346","doi":"https://doi.org/10.1145/3460418.3479375","mag":"3203798346"},"language":"en","primary_location":{"id":"doi:10.1145/3460418.3479375","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3460418.3479375","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers","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/A5083450458","display_name":"Jinhua Su","orcid":"https://orcid.org/0000-0002-6344-0607"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jinhua Su","raw_affiliation_strings":["Renmin University of China, China"],"affiliations":[{"raw_affiliation_string":"Renmin University of China, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100320635","display_name":"Yuanyuan Zhang","orcid":"https://orcid.org/0000-0001-9738-604X"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanyuan Zhang","raw_affiliation_strings":["Beijing Baixingkefu Network Technology Co., Ltd., China"],"affiliations":[{"raw_affiliation_string":"Beijing Baixingkefu Network Technology Co., Ltd., China","institution_ids":["https://openalex.org/I98301712"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5083450458"],"corresponding_institution_ids":["https://openalex.org/I78988378"],"apc_list":null,"apc_paid":null,"fwci":0.5439,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.73045455,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"401","last_page":"407"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9769999980926514,"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"}},"topics":[{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9769999980926514,"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9761000275611877,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9506999850273132,"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/class","display_name":"Class (philosophy)","score":0.6974879503250122},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5534878373146057},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5512399077415466},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48724237084388733},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43891483545303345},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.4340587854385376}],"concepts":[{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.6974879503250122},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5534878373146057},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5512399077415466},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48724237084388733},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43891483545303345},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.4340587854385376}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3460418.3479375","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3460418.3479375","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers","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":17,"referenced_works":["https://openalex.org/W2103614420","https://openalex.org/W2132791018","https://openalex.org/W2146194630","https://openalex.org/W2619201468","https://openalex.org/W2739553566","https://openalex.org/W2883766876","https://openalex.org/W2899151654","https://openalex.org/W2907123474","https://openalex.org/W2908694412","https://openalex.org/W2946229570","https://openalex.org/W2962898354","https://openalex.org/W2972497135","https://openalex.org/W2988098865","https://openalex.org/W2998534535","https://openalex.org/W3084913306","https://openalex.org/W3096412097","https://openalex.org/W3203725677"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W2033914206"],"abstract_inverted_index":{"SHL":[0,34,91],"Recognition":[1],"Challenge":[2],"provides":[3,98],"phone":[4],"sensor":[5,17,100],"data":[6,18,28,50,53,101,108],"for":[7,58,206],"recognizing":[8],"eight":[9],"modes":[10],"of":[11,138],"locomotion":[12],"and":[13,42,45,51,67,88,97,109,115,141,159,179,202],"transportation(activities).":[14],"Most":[15],"motion":[16],"is":[19,36],"user-dependent,":[20],"so":[21],"the":[22,30,65,80,107,120,131,136,166,174,190,195,204,214,217],"model\u2019s":[23],"generalization":[24],"ability":[25,134],"requires":[26],"various":[27],"at":[29,135],"user":[31],"level.":[32],"The":[33,162],"dataset":[35],"collected":[37],"only":[38],"by":[39],"three":[40],"users,":[41],"category":[43,113,139],"imbalance":[44,114,140],"distribution":[46,116,143,196],"gap":[47],"between":[48],"training":[49],"testing":[52],"can":[54,183,199],"be":[55,184,200],"significant":[56],"obstacles":[57],"this":[59,146],"challenge.":[60],"In":[61],"comparison":[62],"to":[63,129,188,193],"exploring":[64,207],"large":[66],"deep":[68],"model":[69,209],"structure,":[70],"some":[71],"model-free":[72],"tricks":[73],"play":[74],"a":[75,150],"more":[76],"critical":[77],"role":[78],"in":[79,102],"previous":[81],"challenges,":[82],"such":[83],"as":[84],"transfer":[85],"learning,":[86],"re-sampling":[87,178],"anti-overfit":[89],"trick.":[90],"challenge":[92],"also":[93],"notices":[94],"that":[95,112,165],"problem":[96],"user-independent":[99],"2021.":[103],"This":[104,122,211],"paper":[105,123,147,212],"analyses":[106],"finds":[110],"out":[111],"inconsistency":[117],"are":[118],"still":[119],"obstacles.":[121],"focuses":[124],"on":[125,173],"evaluating":[126],"different":[127],"methods":[128],"improve":[130],"general":[132],"predicting":[133],"setting":[137],"training-testing":[142],"inconsistency.":[144],"Besides,":[145],"puts":[148],"forward":[149],"new":[151],"ensemble":[152,169],"framework":[153],"called":[154],"triple-O,":[155],"using":[156],"OvO":[157,167],"binarization":[158,168],"one-class":[160],"classification.":[161],"results":[163,172],"show":[164],"gets":[170],"better":[171],"hard-to-distinguish":[175],"class":[176],"than":[177],"re-weighting.":[180],"One-class":[181],"classification":[182],"an":[185],"anomaly":[186],"detection":[187],"re-weight":[189],"meta":[191],"learners":[192],"tackle":[194],"gap.":[197],"Triple-O":[198],"plug-and-play":[201],"pave":[203],"way":[205],"complex":[208],"structures.":[210],"introduces":[213],"solutions":[215],"from":[216],"team":[218],"GoodGoodDriveDayDayUp.":[219]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}