{"id":"https://openalex.org/W2989631814","doi":"https://doi.org/10.1109/icves.2019.8906396","title":"Extreme Gradient Boosting Machine Learning Algorithm For Safe Auto Insurance Operations","display_name":"Extreme Gradient Boosting Machine Learning Algorithm For Safe Auto Insurance Operations","publication_year":2019,"publication_date":"2019-09-01","ids":{"openalex":"https://openalex.org/W2989631814","doi":"https://doi.org/10.1109/icves.2019.8906396","mag":"2989631814"},"language":"en","primary_location":{"id":"doi:10.1109/icves.2019.8906396","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icves.2019.8906396","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Vehicular Electronics and Safety (ICVES)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5003291022","display_name":"Najmeddine Dhieb","orcid":null},"institutions":[{"id":"https://openalex.org/I179097149","display_name":"University of Carthage","ror":"https://ror.org/057x6za15","country_code":"TN","type":"education","lineage":["https://openalex.org/I179097149"]}],"countries":["TN"],"is_corresponding":false,"raw_author_name":"Najmeddine Dhieb","raw_affiliation_strings":["University of Carthage, Higher School of Communications of Tunis,Tunisia","University of Carthage, Higher School of Communications of Tunis, Tunisia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Carthage, Higher School of Communications of Tunis,Tunisia","institution_ids":["https://openalex.org/I179097149"]},{"raw_affiliation_string":"University of Carthage, Higher School of Communications of Tunis, Tunisia","institution_ids":["https://openalex.org/I179097149"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021394822","display_name":"Hakim Ghazzai","orcid":"https://orcid.org/0000-0002-8636-4264"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hakim Ghazzai","raw_affiliation_strings":["School of Systems & Enterprises, Stevens Institute of Technology,Hoboken,NJ,USA","School of Systems & Enterprises, Stevens Institute of Technology, Hoboken, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Systems & Enterprises, Stevens Institute of Technology,Hoboken,NJ,USA","institution_ids":["https://openalex.org/I108468826"]},{"raw_affiliation_string":"School of Systems & Enterprises, Stevens Institute of Technology, Hoboken, NJ, USA","institution_ids":["https://openalex.org/I108468826"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008512450","display_name":"Hichem Besbes","orcid":"https://orcid.org/0000-0003-1063-494X"},"institutions":[{"id":"https://openalex.org/I179097149","display_name":"University of Carthage","ror":"https://ror.org/057x6za15","country_code":"TN","type":"education","lineage":["https://openalex.org/I179097149"]}],"countries":["TN"],"is_corresponding":false,"raw_author_name":"Hichem Besbes","raw_affiliation_strings":["University of Carthage, Higher School of Communications of Tunis,Tunisia","University of Carthage, Higher School of Communications of Tunis, Tunisia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Carthage, Higher School of Communications of Tunis,Tunisia","institution_ids":["https://openalex.org/I179097149"]},{"raw_affiliation_string":"University of Carthage, Higher School of Communications of Tunis, Tunisia","institution_ids":["https://openalex.org/I179097149"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057380866","display_name":"Yehia Massoud","orcid":"https://orcid.org/0000-0002-6701-0639"},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yehia Massoud","raw_affiliation_strings":["School of Systems & Enterprises, Stevens Institute of Technology,Hoboken,NJ,USA","School of Systems & Enterprises, Stevens Institute of Technology, Hoboken, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Systems & Enterprises, Stevens Institute of Technology,Hoboken,NJ,USA","institution_ids":["https://openalex.org/I108468826"]},{"raw_affiliation_string":"School of Systems & Enterprises, Stevens Institute of Technology, Hoboken, NJ, USA","institution_ids":["https://openalex.org/I108468826"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.2943,"has_fulltext":false,"cited_by_count":90,"citation_normalized_percentile":{"value":0.91904367,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9958000183105469,"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":0.9958000183105469,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.98580002784729,"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/T14319","display_name":"Currency Recognition and Detection","score":0.9718000292778015,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.6779782772064209},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6564333438873291},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6164543032646179},{"id":"https://openalex.org/keywords/insurance-policy","display_name":"Insurance policy","score":0.4682541787624359},{"id":"https://openalex.org/keywords/insurance-fraud","display_name":"Insurance fraud","score":0.4679490625858307},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41741734743118286},{"id":"https://openalex.org/keywords/risk-analysis","display_name":"Risk analysis (engineering)","score":0.3425912857055664},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32614001631736755},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.2971499562263489},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.23237085342407227},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.07999742031097412}],"concepts":[{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.6779782772064209},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6564333438873291},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6164543032646179},{"id":"https://openalex.org/C68799949","wikidata":"https://www.wikidata.org/wiki/Q977871","display_name":"Insurance policy","level":2,"score":0.4682541787624359},{"id":"https://openalex.org/C2778976927","wikidata":"https://www.wikidata.org/wiki/Q838081","display_name":"Insurance fraud","level":2,"score":0.4679490625858307},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41741734743118286},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.3425912857055664},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32614001631736755},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.2971499562263489},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.23237085342407227},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.07999742031097412}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icves.2019.8906396","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icves.2019.8906396","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Vehicular Electronics and Safety (ICVES)","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":11,"referenced_works":["https://openalex.org/W2172852798","https://openalex.org/W2295598076","https://openalex.org/W2766703596","https://openalex.org/W2805557887","https://openalex.org/W2810832464","https://openalex.org/W2892493782","https://openalex.org/W2895919328","https://openalex.org/W2906591598","https://openalex.org/W2913138152","https://openalex.org/W2914705570","https://openalex.org/W3102476541"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W2885778889","https://openalex.org/W2766514146","https://openalex.org/W2885516856","https://openalex.org/W4289703016","https://openalex.org/W3094138326","https://openalex.org/W4310224730"],"abstract_inverted_index":{"Vehicle":[0],"safety":[1,108],"is":[2,22,155],"one":[3],"of":[4,23,39,67,88],"the":[5,82,86,93,201],"most":[6],"important":[7],"research":[8],"topics":[9],"not":[10,103],"only":[11,104],"for":[12,17,26,55,141,193],"vehicular":[13],"industry":[14],"but":[15,112],"also":[16,72,113],"auto":[18,142,202],"insurance":[19,56,97,143,203],"companies.":[20],"It":[21],"great":[24],"significance":[25],"them":[27,163],"to":[28,91,105,114,128,156,176,187,223],"avoid":[29],"compensating":[30],"damaged":[31],"vehicles":[32],"and":[33,41,51,58,109,125,161,179,196,218],"dealing":[34],"with":[35,61],"an":[36,136],"incredible":[37],"number":[38],"correct":[40],"fraudulent":[42,46,159,220],"claims.":[43],"In":[44,131],"fact,":[45],"claims":[47,160,221],"present":[48],"a":[49,52,209],"huge":[50],"costly":[53],"problem":[54],"companies":[57,98,144],"end":[59],"up":[60],"big":[62],"losses":[63,199],"reaching":[64],"over":[65],"billions":[66],"Dollars":[68],"yearly.":[69],"These":[70],"frauds":[71],"have":[73],"social-economical":[74],"consequences":[75],"as":[76],"their":[77,89],"costs":[78],"are":[79,99,122,174],"defrayed":[80],"by":[81,214],"policy":[83],"holder":[84],"through":[85],"increase":[87],"premiums":[90],"cover":[92],"insurer":[94],"loss.":[95],"Several":[96],"exploring":[100],"innovative":[101],"solutions":[102],"improve":[106],"customers":[107],"driving":[110],"experience":[111],"streamline":[115],"fraud":[116,138,166],"detection":[117,139],"methods":[118],"since":[119],"traditional":[120],"ones":[121],"complex,":[123],"time-consuming,":[124],"usually":[126],"lead":[127],"inaccurate":[129],"results.":[130],"this":[132,169],"paper,":[133],"we":[134],"develop":[135],"automated":[137],"approach":[140],"based":[145],"on":[146],"extreme":[147],"gradient":[148],"boosting":[149],"algorithm,":[150],"aka":[151],"XGBoost.":[152],"The":[153,183,205],"objective":[154],"automatically":[157],"detect":[158],"classify":[162],"into":[164],"different":[165],"types.":[167],"To":[168],"end,":[170],"data":[171],"analysis":[172],"techniques":[173],"used":[175],"clean,":[177],"explore,":[178],"extract":[180],"relevant":[181],"features.":[182],"proposed":[184],"framework":[185],"aims":[186],"minimize":[188],"human":[189],"intervention,":[190],"deliver":[191],"alerts":[192],"risky":[194],"claims,":[195],"reduce":[197],"monetary":[198],"in":[200,216],"industry.":[204],"obtained":[206],"results":[207],"reveal":[208],"high":[210],"performance":[211],"gain":[212],"achieved":[213],"XGBoost":[215],"detecting":[217],"classifying":[219],"compared":[222],"other":[224],"machine":[225],"learning":[226],"algorithms.":[227]},"counts_by_year":[{"year":2026,"cited_by_count":10},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":28},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2025-10-10T00:00:00"}