{"id":"https://openalex.org/W4388279683","doi":"https://doi.org/10.1109/icbase59196.2023.10303111","title":"Research on UBI Claims Risk Probability Models Based on Machine Learning","display_name":"Research on UBI Claims Risk Probability Models Based on Machine Learning","publication_year":2023,"publication_date":"2023-08-25","ids":{"openalex":"https://openalex.org/W4388279683","doi":"https://doi.org/10.1109/icbase59196.2023.10303111"},"language":"en","primary_location":{"id":"doi:10.1109/icbase59196.2023.10303111","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icbase59196.2023.10303111","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 4th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE)","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/A5056294093","display_name":"Dongyu Li","orcid":"https://orcid.org/0000-0001-8338-0536"},"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":"Dongyu Li","raw_affiliation_strings":["China Automotive Technology & Research Center Co. Ltd,Tianjin,China"],"affiliations":[{"raw_affiliation_string":"China Automotive Technology & Research Center Co. Ltd,Tianjin,China","institution_ids":["https://openalex.org/I4210094894"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100311319","display_name":"Hao Yu","orcid":"https://orcid.org/0000-0003-3855-4722"},"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":"Hao Yu","raw_affiliation_strings":["China Automotive Technology & Research Center Co. Ltd,Tianjin,China"],"affiliations":[{"raw_affiliation_string":"China Automotive Technology & Research Center Co. Ltd,Tianjin,China","institution_ids":["https://openalex.org/I4210094894"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064534354","display_name":"Xu Zhu","orcid":"https://orcid.org/0000-0003-1287-1623"},"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":"Xu Zhu","raw_affiliation_strings":["China Automotive Technology & Research Center Co. Ltd,Tianjin,China"],"affiliations":[{"raw_affiliation_string":"China Automotive Technology & Research Center Co. Ltd,Tianjin,China","institution_ids":["https://openalex.org/I4210094894"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5056294093"],"corresponding_institution_ids":["https://openalex.org/I4210094894"],"apc_list":null,"apc_paid":null,"fwci":0.2076,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.51061825,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"354","last_page":"358"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10370","display_name":"Traffic and Road Safety","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10370","display_name":"Traffic and Road Safety","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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":0.9814000129699707,"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"}},{"id":"https://openalex.org/T12394","display_name":"Insurance and Financial Risk Management","score":0.9426000118255615,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/automobile-insurance","display_name":"Automobile insurance","score":0.8018920421600342},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6305640935897827},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6153086423873901},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.5994166731834412},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5455431938171387},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4910256862640381},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4603748917579651},{"id":"https://openalex.org/keywords/accident","display_name":"Accident (philosophy)","score":0.4487525224685669},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38925448060035706},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3069498836994171},{"id":"https://openalex.org/keywords/actuarial-science","display_name":"Actuarial science","score":0.2510506510734558},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.12149259448051453}],"concepts":[{"id":"https://openalex.org/C2992945734","wikidata":"https://www.wikidata.org/wiki/Q1068361","display_name":"Automobile insurance","level":2,"score":0.8018920421600342},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6305640935897827},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6153086423873901},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.5994166731834412},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5455431938171387},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4910256862640381},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4603748917579651},{"id":"https://openalex.org/C2780289543","wikidata":"https://www.wikidata.org/wiki/Q424630","display_name":"Accident (philosophy)","level":2,"score":0.4487525224685669},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38925448060035706},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3069498836994171},{"id":"https://openalex.org/C162118730","wikidata":"https://www.wikidata.org/wiki/Q1128453","display_name":"Actuarial science","level":1,"score":0.2510506510734558},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.12149259448051453},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icbase59196.2023.10303111","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icbase59196.2023.10303111","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 4th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.550000011920929}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":2,"referenced_works":["https://openalex.org/W2073067026","https://openalex.org/W2292324303"],"related_works":["https://openalex.org/W4390608645","https://openalex.org/W4247566972","https://openalex.org/W4394895745","https://openalex.org/W2960264696","https://openalex.org/W4367335965","https://openalex.org/W4385574838","https://openalex.org/W4367335937","https://openalex.org/W4308573183","https://openalex.org/W4321504903","https://openalex.org/W3200331266"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"with":[3,61],"the":[4,10,13,42,54,62,74,83,91,104,150,153,157,176,183],"development":[5],"of":[6,12,15,44,94,152,179],"big":[7],"data":[8,93],"and":[9,23,25,38,49,98,109,120,171,181],"popularization":[11],"Internet":[14],"vehicles,":[16],"driving":[17,66,87,96,177],"behaviors":[18,97],"can":[19,174],"be":[20],"effectively":[21],"recorded":[22],"analyzed,":[24],"applied":[26],"in":[27],"various":[28],"automotive":[29],"post-market":[30],"fields,":[31],"including":[32],"insurance":[33,51,165],"pricing,":[34],"model":[35,108,122,138],"risk":[36,75,129,158],"classification,":[37],"so":[39],"on.":[40],"With":[41],"reform":[43],"commercial":[45],"vehicle":[46],"rates,":[47],"more":[48,50,70],"companies":[52,166],"have":[53],"power":[55],"to":[56,81,123,148,156,160,167],"set":[57],"their":[58,125],"price.":[59],"Compared":[60],"traditional":[63,105],"influencing":[64,154],"factors,":[65],"behavior":[67,178],"has":[68,139],"a":[69,162],"significant":[71],"impact":[72],"on":[73,86,90,128],"probability.":[76,130],"Therefore,":[77,143],"it":[78,133,173],"is":[79,134,146],"necessary":[80],"adjust":[82],"premium":[84],"based":[85],"behavior.":[88],"Based":[89],"collected":[92],"100":[95],"accident":[99,185],"records,":[100],"this":[101,144],"paper":[102],"uses":[103],"Logistic":[106],"Regression":[107],"three":[110],"popular":[111],"machine":[112],"learning":[113],"algorithms:":[114],"KNN":[115],"algorithm,":[116,119],"Random":[117],"Forest":[118],"XGBoost":[121,137],"compare":[124],"prediction":[126,141],"results":[127],"By":[131],"comparison,":[132],"found":[135],"that":[136],"higher":[140],"accuracy.":[142],"algorithm":[145],"used":[147],"rank":[149],"importance":[151],"factors":[155],"probability,":[159],"provide":[161],"basis":[163],"for":[164],"formulate":[168],"subsequent":[169],"premiums,":[170],"also,":[172],"improve":[175],"drivers,":[180],"reduce":[182],"traffic":[184],"rate.":[186]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}