{"id":"https://openalex.org/W4387421376","doi":"https://doi.org/10.1145/3594739.3610756","title":"An Ensemble Framework Based on Fine Multi-Window Feature Engineering and Overfitting Prevention for Transportation Mode Recognition","display_name":"An Ensemble Framework Based on Fine Multi-Window Feature Engineering and Overfitting Prevention for Transportation Mode Recognition","publication_year":2023,"publication_date":"2023-10-07","ids":{"openalex":"https://openalex.org/W4387421376","doi":"https://doi.org/10.1145/3594739.3610756"},"language":"en","primary_location":{"id":"doi:10.1145/3594739.3610756","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3594739.3610756","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 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing &amp; the 2023 ACM International Symposium on Wearable Computing","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/A5060743052","display_name":"Zehong Zeng","orcid":"https://orcid.org/0009-0000-0482-6588"},"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":"Zehong Zeng","raw_affiliation_strings":["Center for Applied Statistics, School of Statistics Renmin University of China, China"],"raw_orcid":"https://orcid.org/0009-0000-0482-6588","affiliations":[{"raw_affiliation_string":"Center for Applied Statistics, School of Statistics Renmin University of China, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103245274","display_name":"Yueyang Liu","orcid":"https://orcid.org/0009-0004-7068-998X"},"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":false,"raw_author_name":"Yueyang Liu","raw_affiliation_strings":["Center for Applied Statistics, School of Statistics Renmin University of China, China"],"raw_orcid":"https://orcid.org/0009-0004-7068-998X","affiliations":[{"raw_affiliation_string":"Center for Applied Statistics, School of Statistics Renmin University of China, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088837345","display_name":"X. J. Lu","orcid":null},"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":false,"raw_author_name":"Xiaoshi Lu","raw_affiliation_strings":["Center for Applied Statistics, School of Statistics Renmin University of China, China"],"raw_orcid":"https://orcid.org/0009-0003-3777-2573","affiliations":[{"raw_affiliation_string":"Center for Applied Statistics, School of Statistics Renmin University of China, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101414639","display_name":"Yuanyuan Zhang","orcid":"https://orcid.org/0009-0000-2320-0169"},"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"],"raw_orcid":"https://orcid.org/0009-0000-2320-0169","affiliations":[{"raw_affiliation_string":"Beijing Baixingkefu Network Technology Co., Ltd., China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101601790","display_name":"Xiaoling Lu","orcid":"https://orcid.org/0000-0002-1854-2532"},"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":false,"raw_author_name":"Xiaoling Lu","raw_affiliation_strings":["Center for Applied Statistics, School of Statistics Renmin University of China, China"],"raw_orcid":"https://orcid.org/0000-0002-1854-2532","affiliations":[{"raw_affiliation_string":"Center for Applied Statistics, School of Statistics Renmin University of China, China","institution_ids":["https://openalex.org/I78988378"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5060743052"],"corresponding_institution_ids":["https://openalex.org/I78988378"],"apc_list":null,"apc_paid":null,"fwci":1.5206,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.84646865,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"563","last_page":"568"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9929999709129333,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.945778489112854},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7581416368484497},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6180837154388428},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5882644653320312},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5759791135787964},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.5498042106628418},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5468302965164185},{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.49945545196533203},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48577383160591125},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4621945023536682},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4544317424297333},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4174152612686157},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.22665223479270935},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.14312729239463806}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.945778489112854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7581416368484497},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6180837154388428},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5882644653320312},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5759791135787964},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.5498042106628418},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5468302965164185},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.49945545196533203},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48577383160591125},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4621945023536682},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4544317424297333},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4174152612686157},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.22665223479270935},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.14312729239463806},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3594739.3610756","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3594739.3610756","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 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing &amp; the 2023 ACM International Symposium on Wearable Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2883766876","https://openalex.org/W2907123474","https://openalex.org/W3084453745","https://openalex.org/W3085353868","https://openalex.org/W3085412470","https://openalex.org/W3085994804","https://openalex.org/W3086681538","https://openalex.org/W3202752967","https://openalex.org/W3203798346","https://openalex.org/W4387421349"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W1038900426","https://openalex.org/W2944292463","https://openalex.org/W3014252901","https://openalex.org/W2188759683","https://openalex.org/W4317376680","https://openalex.org/W3094256312","https://openalex.org/W4360777922","https://openalex.org/W3208169454"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"our":[3],"solution":[4],"to":[5,64,74,98,121],"the":[6,56,118,123],"SHL":[7],"recognition":[8],"challenge":[9],"2023":[10],"which":[11],"focuses":[12],"on":[13,23,37,117,132],"recognizing":[14],"8":[15],"transportation":[16],"modes":[17],"in":[18,55],"a":[19,48],"user-independent":[20],"manner":[21],"based":[22,36],"motion":[24],"and":[25,42,50,69,78,110],"GPS":[26],"sensor":[27],"data.":[28],"Our":[29],"team":[30],"ZZL":[31],"propose":[32],"an":[33,90],"ensemble":[34,91],"framework":[35,92],"fine":[38],"multi-window":[39],"feature":[40,57,85],"engineering":[41,58],"overfitting":[43],"prevention.":[44],"Firstly,":[45],"we":[46,88,114,127],"extracted":[47],"large":[49],"diverse":[51],"set":[52],"of":[53,130],"features":[54],"process,":[59],"including":[60,101],"incorporating":[61],"OpenStreetMap":[62],"data":[63,102,105],"better":[65],"leverage":[66],"location":[67],"data,":[68],"introducing":[70],"multiple":[71],"time":[72],"windows":[73],"extract":[75],"long,":[76],"medium,":[77],"short":[79],"term":[80],"aggregated":[81],"features,":[82],"providing":[83],"rich":[84],"inputs.":[86],"Secondly,":[87],"proposed":[89],"that":[93],"comprehensively":[94],"utilizes":[95],"different":[96],"techniques":[97],"prevent":[99],"overfitting,":[100],"downsampling,":[103],"fine-tuning":[104],"distribution,":[106],"designed":[107],"train-test":[108],"splitting,":[109],"model":[111,119],"integration.":[112],"Moreover,":[113],"applied":[115],"post-processing":[116],"predictions":[120],"smooth":[122],"predicted":[124],"results.":[125],"Finally,":[126],"achieve":[128],"F1-score":[129],"0.868":[131],"validation":[133],"dataset.":[134]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
