{"id":"https://openalex.org/W7151254147","doi":"https://doi.org/10.1109/icmla66185.2025.00082","title":"Applying Machine Learning to Classify Automobile Repossession Success","display_name":"Applying Machine Learning to Classify Automobile Repossession Success","publication_year":2025,"publication_date":"2025-12-03","ids":{"openalex":"https://openalex.org/W7151254147","doi":"https://doi.org/10.1109/icmla66185.2025.00082"},"language":null,"primary_location":{"id":"doi:10.1109/icmla66185.2025.00082","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00082","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","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/A5125720870","display_name":"Preston Billion-Polak","orcid":null},"institutions":[{"id":"https://openalex.org/I63772739","display_name":"Florida Atlantic University","ror":"https://ror.org/05p8w6387","country_code":"US","type":"education","lineage":["https://openalex.org/I63772739"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Preston Billion-Polak","raw_affiliation_strings":["Florida Atlantic University,Boca Raton,FL,United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Florida Atlantic University,Boca Raton,FL,United States","institution_ids":["https://openalex.org/I63772739"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112783249","display_name":"Andy Sinclair","orcid":null},"institutions":[{"id":"https://openalex.org/I63772739","display_name":"Florida Atlantic University","ror":"https://ror.org/05p8w6387","country_code":"US","type":"education","lineage":["https://openalex.org/I63772739"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andy Sinclair","raw_affiliation_strings":["Florida Atlantic University,Boca Raton,FL,United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Florida Atlantic University,Boca Raton,FL,United States","institution_ids":["https://openalex.org/I63772739"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089170562","display_name":"Taghi M. Khoshgoftaar","orcid":null},"institutions":[{"id":"https://openalex.org/I63772739","display_name":"Florida Atlantic University","ror":"https://ror.org/05p8w6387","country_code":"US","type":"education","lineage":["https://openalex.org/I63772739"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Taghi Khoshgoftaar","raw_affiliation_strings":["Florida Atlantic University,Boca Raton,FL,United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Florida Atlantic University,Boca Raton,FL,United States","institution_ids":["https://openalex.org/I63772739"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5125720870"],"corresponding_institution_ids":["https://openalex.org/I63772739"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.85957779,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.2840000092983246,"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":0.2840000092983246,"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/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.18529999256134033,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.06610000133514404,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.2935999929904938},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.2870999872684479},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.28439998626708984},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.2547000050544739}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4943999946117401},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4871000051498413},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4643999934196472},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.37299999594688416},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2870999872684479},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.22759999334812164},{"id":"https://openalex.org/C77595967","wikidata":"https://www.wikidata.org/wiki/Q3151013","display_name":"Forensic engineering","level":1,"score":0.2232999950647354}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmla66185.2025.00082","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00082","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","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/W1966716734","https://openalex.org/W2115098571","https://openalex.org/W2295598076","https://openalex.org/W2899434936","https://openalex.org/W2987201163","https://openalex.org/W3094948551","https://openalex.org/W3181414820","https://openalex.org/W4234698323","https://openalex.org/W4321333028","https://openalex.org/W4392242357","https://openalex.org/W4410398006"],"related_works":[],"abstract_inverted_index":{"Automobile":[0],"repossession":[1,27,52,92,99,165,204],"and":[2,10,16,58,106,115,124,154,167,173,208],"impoundment":[3],"is":[4,177],"crucial":[5],"to":[6,54,88,126,193,214],"enforcing":[7],"loan":[8],"repayments":[9],"the":[11,81,199],"law,":[12],"but":[13],"remains":[14,205],"unpredictable":[15],"challenging.":[17],"A":[18],"wide":[19],"range":[20],"of":[21,83,202],"factors":[22],"can":[23],"prevent":[24],"a":[25,47,89,111,135],"successful":[26],"attempt,":[28],"such":[29],"as":[30],"miscommunication":[31],"on":[32,96],"vehicle":[33],"location":[34],"or":[35,38,70,137,220],"lien":[36],"status,":[37,105],"debtor":[39],"hostility.":[40],"Automated":[41],"risk":[42],"estimation":[43],"models":[44,72],"may":[45,168,189],"provide":[46,161,190],"valuable":[48,162],"resource":[49,174],"in":[50,73],"allowing":[51],"companies":[53,166],"evaluate":[55],"different":[56],"jobs":[57],"allocate":[59],"resources":[60],"effectively;":[61],"however,":[62],"we":[63,79,209],"find":[64,140],"no":[65],"existing":[66],"work":[67,213],"investigating":[68],"statistical":[69],"machine-learning-based":[71],"this":[74,77,225],"field.":[75,226],"To":[76],"end,":[78],"investigate":[80],"application":[82,200],"machine":[84,217],"learning":[85,218],"classification":[86,149],"techniques":[87,223],"private":[90],"auto":[91,203],"dataset":[93],"containing":[94],"information":[95,163],"200,000":[97],"distinct":[98],"attempts,":[100],"including":[101],"location,":[102],"license":[103],"plate":[104],"address":[107],"type.":[108],"We":[109,139],"train":[110],"logistic":[112],"regression":[113],"classifier":[114],"two":[116],"Gradient":[117],"Boosted":[118],"Decision":[119],"Tree":[120],"(GBDT)":[121],"algorithms":[122],"(CatBoost":[123],"XGBoost)":[125],"predict":[127],"whether":[128],"any":[129],"given":[130],"repo":[131],"attempt":[132],"will":[133],"be":[134],"success":[136],"failure.":[138],"that":[141,159],"all":[142],"three":[143],"models,":[144,187],"when":[145,195],"optimized,":[146],"give":[147],"moderate":[148],"performance":[150],"(almost":[151],"80%":[152],"accuracy":[153],">0.50":[155],"F1":[156],"score),":[157],"indicating":[158],"they":[160],"for":[164],"support":[169],"more":[170],"effective":[171],"decision-making":[172],"allocation.":[175],"This":[176],"supported":[178],"by":[179],"extracted":[180],"feature":[181],"importance":[182],"values":[183],"from":[184],"our":[185],"GBDT":[186],"which":[188],"additional":[191],"insights":[192],"practitioners":[194],"prioritizing":[196],"assignments.":[197],"However,":[198],"domain":[201],"largely":[206],"unexplored,":[207],"highly":[210],"encourage":[211],"future":[212],"explore":[215],"other":[216],"approaches":[219],"data":[221],"collection":[222],"within":[224]},"counts_by_year":[],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2026-04-08T00:00:00"}