{"id":"https://openalex.org/W4392583773","doi":"https://doi.org/10.1145/3640824.3640857","title":"A comparative study of machine learning based importance measures for car insurance pricing factors","display_name":"A comparative study of machine learning based importance measures for car insurance pricing factors","publication_year":2024,"publication_date":"2024-01-26","ids":{"openalex":"https://openalex.org/W4392583773","doi":"https://doi.org/10.1145/3640824.3640857"},"language":"en","primary_location":{"id":"doi:10.1145/3640824.3640857","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3640824.3640857","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 8th International Conference on Control Engineering and Artificial Intelligence","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/A5011020522","display_name":"Qianqian Zhu","orcid":"https://orcid.org/0009-0008-6963-3355"},"institutions":[{"id":"https://openalex.org/I4210094894","display_name":"China Automotive Technology and Research Center","ror":"https://ror.org/00r5r6807","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210094894"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qianqian Zhu","raw_affiliation_strings":["Automotive Data of China Co.,Ltd., China and \rChina Automotive Technology and Research Center Co.,Ltd., China"],"raw_orcid":"https://orcid.org/0009-0008-6963-3355","affiliations":[{"raw_affiliation_string":"Automotive Data of China Co.,Ltd., China and \rChina Automotive Technology and Research Center Co.,Ltd., China","institution_ids":["https://openalex.org/I4210094894"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010249969","display_name":"Xuening Wu","orcid":"https://orcid.org/0009-0001-8609-5850"},"institutions":[{"id":"https://openalex.org/I4210094894","display_name":"China Automotive Technology and Research Center","ror":"https://ror.org/00r5r6807","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210094894"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuening Wu","raw_affiliation_strings":["Automotive Data of China Co.,Ltd., China and \rChina Automotive Technology and Research Center Co.,Ltd., China"],"raw_orcid":"https://orcid.org/0009-0001-8609-5850","affiliations":[{"raw_affiliation_string":"Automotive Data of China Co.,Ltd., China and \rChina Automotive Technology and Research Center Co.,Ltd., China","institution_ids":["https://openalex.org/I4210094894"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111142661","display_name":"Yinnan Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210094894","display_name":"China Automotive Technology and Research Center","ror":"https://ror.org/00r5r6807","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210094894"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yinnan Liu","raw_affiliation_strings":["Automotive Data of China Co.,Ltd., China and \rChina Automotive Technology and Research Center Co.,Ltd., China"],"raw_orcid":"https://orcid.org/0009-0001-8176-4525","affiliations":[{"raw_affiliation_string":"Automotive Data of China Co.,Ltd., China and \rChina Automotive Technology and Research Center Co.,Ltd., China","institution_ids":["https://openalex.org/I4210094894"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5094097098","display_name":"Jiayin Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210094894","display_name":"China Automotive Technology and Research Center","ror":"https://ror.org/00r5r6807","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210094894"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiayin Huang","raw_affiliation_strings":["Automotive Data of China Co.,Ltd., China and \rChina Automotive Technology and Research Center Co.,Ltd., China"],"raw_orcid":"https://orcid.org/0009-0007-4082-8661","affiliations":[{"raw_affiliation_string":"Automotive Data of China Co.,Ltd., China and \rChina Automotive Technology and Research Center Co.,Ltd., China","institution_ids":["https://openalex.org/I4210094894"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5011020522"],"corresponding_institution_ids":["https://openalex.org/I4210094894"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.03631657,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"209","last_page":"214"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12394","display_name":"Insurance and Financial Risk Management","score":0.9416999816894531,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12394","display_name":"Insurance and Financial Risk Management","score":0.9416999816894531,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12011","display_name":"Insurance, Mortality, Demography, Risk Management","score":0.9093000292778015,"subfield":{"id":"https://openalex.org/subfields/3317","display_name":"Demography"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5547385215759277},{"id":"https://openalex.org/keywords/automobile-insurance","display_name":"Automobile insurance","score":0.4210861027240753},{"id":"https://openalex.org/keywords/actuarial-science","display_name":"Actuarial science","score":0.3427221179008484},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.28479719161987305}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5547385215759277},{"id":"https://openalex.org/C2992945734","wikidata":"https://www.wikidata.org/wiki/Q1068361","display_name":"Automobile insurance","level":2,"score":0.4210861027240753},{"id":"https://openalex.org/C162118730","wikidata":"https://www.wikidata.org/wiki/Q1128453","display_name":"Actuarial science","level":1,"score":0.3427221179008484},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.28479719161987305}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3640824.3640857","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3640824.3640857","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 8th International Conference on Control Engineering and Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.699999988079071,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2036539394","https://openalex.org/W2487492882","https://openalex.org/W2797728052","https://openalex.org/W2947666609","https://openalex.org/W3010791620","https://openalex.org/W6750773745","https://openalex.org/W6767406180","https://openalex.org/W6807799880"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2982132416","https://openalex.org/W567273944","https://openalex.org/W2159335279","https://openalex.org/W38564056","https://openalex.org/W2100991632","https://openalex.org/W3159078436"],"abstract_inverted_index":{"With":[0],"the":[1,40,63,72,117,136,140,146],"rapid":[2],"development":[3],"of":[4,28,65,74,120],"machine-learning":[5,13],"technology,":[6],"more":[7,9,52],"and":[8,33,49,54,91,93,108,114,142,145,171,182,191],"insurers":[10,32,181],"are":[11],"applying":[12],"methods":[14,69,82],"to":[15,31,42,61,94,166,186],"improve":[16],"their":[17],"car":[18,23,34,75,103,121,187],"insurance":[19,24,47,56,76,104,122,188],"pricing":[20,25,77,123,189],"strategies.":[21,57],"Measuring":[22],"factors":[26,45,124,130,157],"is":[27],"great":[29],"importance":[30,73,133],"owners,":[35],"as":[36,84,139],"it":[37],"can":[38],"reveal":[39],"extent":[41],"which":[43,162],"different":[44,66,126],"affect":[46],"premiums":[48],"help":[50],"develop":[51],"accurate":[53],"personalized":[55],"This":[58],"study":[59,98],"aims":[60],"compare":[62],"performance":[64],"machine":[67],"learning":[68],"in":[70,116,168],"measuring":[71],"factors,":[78],"focusing":[79],"on":[80,100],"standard":[81],"such":[83,138],"generalized":[85],"linear":[86],"models":[87,190],"(GLM),":[88],"random":[89],"forests,":[90],"XGBoost,":[92],"conduct":[95],"an":[96,177],"empirical":[97],"based":[99],"two":[101],"real":[102],"datasets.":[105],"Through":[106],"experiments":[107],"data":[109,172],"analysis,":[110],"we":[111],"find":[112],"consistency":[113],"variability":[115],"essential":[118,178],"measures":[119,134,175],"across":[125,135,160],"algorithmic":[127],"models.":[128],"Some":[129],"have":[131],"consistent":[132],"models,":[137,161],"reward":[141],"penalty":[143],"coefficients":[144],"manufacturer's":[147],"guide":[148,183],"price.":[149],"However,":[150],"there":[151],"were":[152,158],"also":[153],"instances":[154],"where":[155],"some":[156],"inconsistent":[159],"may":[163],"be":[164],"due":[165],"differences":[167],"model":[169],"algorithms":[170],"characteristics.":[173],"These":[174],"provide":[176],"reference":[179],"for":[180],"further":[184],"improvements":[185],"methods.":[192]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
