{"id":"https://openalex.org/W4313451513","doi":"https://doi.org/10.1109/iccr56254.2022.9995969","title":"Retracted: An Effective Approach for the Prediction of Car Loan Default Based-on Accuracy, Precision, Recall Using Extreme Logistic Regression Algorithm and K-Nearest Neighbors Algorithm on Financial Institution Loan Dataset","display_name":"Retracted: An Effective Approach for the Prediction of Car Loan Default Based-on Accuracy, Precision, Recall Using Extreme Logistic Regression Algorithm and K-Nearest Neighbors Algorithm on Financial Institution Loan Dataset","publication_year":2022,"publication_date":"2022-10-06","ids":{"openalex":"https://openalex.org/W4313451513","doi":"https://doi.org/10.1109/iccr56254.2022.9995969"},"language":"en","primary_location":{"id":"doi:10.1109/iccr56254.2022.9995969","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccr56254.2022.9995969","pdf_url":null,"source":{"id":"https://openalex.org/S4363608155","display_name":"2022 International Conference on Cyber Resilience (ICCR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Conference on Cyber Resilience (ICCR)","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/A5043014319","display_name":"Dadithota Jaya Prakash Reddy","orcid":null},"institutions":[{"id":"https://openalex.org/I85461943","display_name":"Saveetha University","ror":"https://ror.org/0034me914","country_code":"IN","type":"education","lineage":["https://openalex.org/I85461943"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Dadithota Jaya Prakash Reddy","raw_affiliation_strings":["Saveetha University,Department of computer science and Engineering,Chennai,India","Department of computer science and Engineering, Saveetha University, Chennai, India"],"affiliations":[{"raw_affiliation_string":"Saveetha University,Department of computer science and Engineering,Chennai,India","institution_ids":["https://openalex.org/I85461943"]},{"raw_affiliation_string":"Department of computer science and Engineering, Saveetha University, Chennai, India","institution_ids":["https://openalex.org/I85461943"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056318419","display_name":"M. Gunasekaran","orcid":"https://orcid.org/0000-0002-2908-3052"},"institutions":[{"id":"https://openalex.org/I85461943","display_name":"Saveetha University","ror":"https://ror.org/0034me914","country_code":"IN","type":"education","lineage":["https://openalex.org/I85461943"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"M. Gunasekaran","raw_affiliation_strings":["Saveetha University,Department of computer science and Engineering,Chennai,India","Department of computer science and Engineering, Saveetha University, Chennai, India"],"affiliations":[{"raw_affiliation_string":"Saveetha University,Department of computer science and Engineering,Chennai,India","institution_ids":["https://openalex.org/I85461943"]},{"raw_affiliation_string":"Department of computer science and Engineering, Saveetha University, Chennai, India","institution_ids":["https://openalex.org/I85461943"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015638182","display_name":"Shantha Sundari","orcid":"https://orcid.org/0000-0002-9468-2625"},"institutions":[{"id":"https://openalex.org/I85461943","display_name":"Saveetha University","ror":"https://ror.org/0034me914","country_code":"IN","type":"education","lineage":["https://openalex.org/I85461943"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"K.K. Shantha Sundari","raw_affiliation_strings":["Saveetha University,Department of computer science and Engineering,Chennai,India","Department of computer science and Engineering, Saveetha University, Chennai, India"],"affiliations":[{"raw_affiliation_string":"Saveetha University,Department of computer science and Engineering,Chennai,India","institution_ids":["https://openalex.org/I85461943"]},{"raw_affiliation_string":"Department of computer science and Engineering, Saveetha University, Chennai, India","institution_ids":["https://openalex.org/I85461943"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5043014319"],"corresponding_institution_ids":["https://openalex.org/I85461943"],"apc_list":null,"apc_paid":null,"fwci":1.7684,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.85933504,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":true,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9886999726295471,"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"}},"topics":[{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9886999726295471,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9843000173568726,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9753000140190125,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.7275661826133728},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.6929281949996948},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6030694246292114},{"id":"https://openalex.org/keywords/loan","display_name":"Loan","score":0.5882824659347534},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.5297982096672058},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.492962121963501},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45018327236175537},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4159086048603058},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.4159030318260193},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4022953510284424},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3643306493759155},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.3183344900608063},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.06124439835548401}],"concepts":[{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.7275661826133728},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.6929281949996948},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6030694246292114},{"id":"https://openalex.org/C2777764128","wikidata":"https://www.wikidata.org/wiki/Q189539","display_name":"Loan","level":2,"score":0.5882824659347534},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.5297982096672058},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.492962121963501},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45018327236175537},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4159086048603058},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.4159030318260193},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4022953510284424},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3643306493759155},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.3183344900608063},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.06124439835548401},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccr56254.2022.9995969","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccr56254.2022.9995969","pdf_url":null,"source":{"id":"https://openalex.org/S4363608155","display_name":"2022 International Conference on Cyber Resilience (ICCR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Conference on Cyber Resilience (ICCR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.44999998807907104,"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1992306630","https://openalex.org/W2077806830","https://openalex.org/W2487654791","https://openalex.org/W2501800671","https://openalex.org/W2557163999","https://openalex.org/W2806864977","https://openalex.org/W2909726747","https://openalex.org/W2965055566","https://openalex.org/W2972948290","https://openalex.org/W3024207678","https://openalex.org/W3103942004","https://openalex.org/W3122620347","https://openalex.org/W3133649672","https://openalex.org/W3210834507","https://openalex.org/W4213129301","https://openalex.org/W4220654399","https://openalex.org/W4221061746","https://openalex.org/W4256243641","https://openalex.org/W4292622093"],"related_works":["https://openalex.org/W2129863591","https://openalex.org/W1596769710","https://openalex.org/W2923497059","https://openalex.org/W2978363435","https://openalex.org/W2785302227","https://openalex.org/W3217448422","https://openalex.org/W4362499272","https://openalex.org/W3103207359","https://openalex.org/W2158177428","https://openalex.org/W2550798287"],"abstract_inverted_index":{"The":[0,94,105],"objective":[1],"of":[2,100,110,138,157],"the":[3,40,50,101,129,133,154,162,179],"work":[4],"is":[5,26,103],"Car":[6,125,148],"Loan":[7,126,149],"Forecasting":[8,127,150],"Using":[9],"an":[10,27,81,173],"Extreme":[11,41,82,174],"Logistic":[12,42,83,111,175],"Regression":[13,43,84,112,176],"algorithm":[14,44,53,85,141,177,182],"with":[15,45,86,114,168],"novel":[16,46,87],"credal":[17,47,88],"sets":[18,89],"and":[19,49,90,108,121,136,145,171],"K-Nearest":[20,51,91,139,180],"Neighbors":[21,92,140,181],"algorithm.":[22,93],"Machine":[23],"Learning":[24],"Techniques":[25],"emerging":[28],"research":[29,74],"field":[30],"that":[31],"can":[32],"be":[33],"used":[34,131],"for":[35,57,98],"predictions.":[36],"When":[37,166],"compared":[38],"to":[39,61],"sets,":[48],"neighbors":[52],"uses":[54],"a":[55],"framework":[56],"vehicle":[58],"loan":[59,63],"prediction":[60],"predict":[62],"default":[64],"from":[65,161],"banks":[66],"while":[67],"clients":[68],"are":[69],"facing":[70],"significant":[71],"challenges.":[72],"This":[73],"study":[75],"considers":[76],"two":[77],"groups":[78],"such":[79],"as":[80],"sample":[95,164],"size":[96],"considered":[97],"each":[99],"algorithms":[102],"20.":[104],"accuracy,":[106,169],"precision,":[107,170],"recall":[109],"Algorithm":[113],"Novel":[115],"Credal":[116],"Set":[117],"produce":[118],"73.86%,":[119],"17.75%":[120],"24.73%":[122],"respectively":[123,147],"in":[124],"on":[128],"dataset":[130],"whereas":[132],"Accuracy,":[134],"Precision,":[135],"Recall":[137],"produces":[142],"67.86%,":[143],"16.67%,":[144],"21.86%":[146],"default.":[151],"It":[152],"shows":[153],"statistical":[155],"significance":[156],"(p":[158],"\u00bf":[159],"0.001)":[160],"independent":[163],"T-test.":[165],"comparing":[167],"recall,":[172],"outperformed":[178],"approach":[183],"sub-stantially.":[184]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2023-01-06T00:00:00"}