{"id":"https://openalex.org/W4412030396","doi":"https://doi.org/10.1109/access.2025.3585980","title":"Optimizing XGBoost Hyperparameters for Credit Scoring Classification Using Weighted Cognitive Avoidance Particle Swarm","display_name":"Optimizing XGBoost Hyperparameters for Credit Scoring Classification Using Weighted Cognitive Avoidance Particle Swarm","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4412030396","doi":"https://doi.org/10.1109/access.2025.3585980"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3585980","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3585980","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3585980","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052249200","display_name":"Atul Vikas Lakra","orcid":"https://orcid.org/0000-0002-1911-7263"},"institutions":[{"id":"https://openalex.org/I185065464","display_name":"Veer Surendra Sai University of Technology","ror":"https://ror.org/02yghbg68","country_code":"IN","type":"education","lineage":["https://openalex.org/I185065464"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Atul Vikas Lakra","raw_affiliation_strings":["Department of Computer Science and Engineering, Veer Surendra Sai University of Technology, Burla, Odisha, India","Veer Surendra Sai University of Technology, Burla, Odisha, India"],"raw_orcid":"https://orcid.org/0000-0002-1911-7263","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Veer Surendra Sai University of Technology, Burla, Odisha, India","institution_ids":["https://openalex.org/I185065464"]},{"raw_affiliation_string":"Veer Surendra Sai University of Technology, Burla, Odisha, India","institution_ids":["https://openalex.org/I185065464"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058792267","display_name":"Sudarson Jena","orcid":"https://orcid.org/0000-0002-3870-5941"},"institutions":[{"id":"https://openalex.org/I112754052","display_name":"Sambalpur University","ror":"https://ror.org/04s222234","country_code":"IN","type":"education","lineage":["https://openalex.org/I112754052"]},{"id":"https://openalex.org/I4210146312","display_name":"Sambalpur University Institute of Information Technology","ror":"https://ror.org/05jnwy894","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210146312"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sudarson Jena","raw_affiliation_strings":["Sambalpur University Institute of Information Technology, Sambalpur University, Burla, Odisha, India"],"raw_orcid":"https://orcid.org/0000-0002-3870-5941","affiliations":[{"raw_affiliation_string":"Sambalpur University Institute of Information Technology, Sambalpur University, Burla, Odisha, India","institution_ids":["https://openalex.org/I112754052","https://openalex.org/I4210146312"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071356251","display_name":"Kaushik Mishra","orcid":"https://orcid.org/0000-0001-9499-0727"},"institutions":[{"id":"https://openalex.org/I164861460","display_name":"Manipal Academy of Higher Education","ror":"https://ror.org/02xzytt36","country_code":"IN","type":"education","lineage":["https://openalex.org/I164861460"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kaushik Mishra","raw_affiliation_strings":["Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India"],"raw_orcid":"https://orcid.org/0000-0001-9499-0727","affiliations":[{"raw_affiliation_string":"Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India","institution_ids":["https://openalex.org/I164861460"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5052249200"],"corresponding_institution_ids":["https://openalex.org/I185065464"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":6.6048,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.9613251,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":"13","issue":null,"first_page":"127292","last_page":"127306"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9817000031471252,"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":0.9817000031471252,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.8164597153663635},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.7023631930351257},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5807477831840515},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48985645174980164},{"id":"https://openalex.org/keywords/cognition","display_name":"Cognition","score":0.4223599135875702},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37665337324142456},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.08797392249107361},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.06642615795135498}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.8164597153663635},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.7023631930351257},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5807477831840515},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48985645174980164},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.4223599135875702},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37665337324142456},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.08797392249107361},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.06642615795135498}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3585980","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3585980","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:03f47b0d33524c4484f5edf1227fe4aa","is_oa":true,"landing_page_url":"https://doaj.org/article/03f47b0d33524c4484f5edf1227fe4aa","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 127292-127306 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3585980","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3585980","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.5099999904632568,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1678356000","https://openalex.org/W1973704036","https://openalex.org/W1976744965","https://openalex.org/W1991383297","https://openalex.org/W2015954856","https://openalex.org/W2024365709","https://openalex.org/W2026097810","https://openalex.org/W2070236178","https://openalex.org/W2131816657","https://openalex.org/W2139364554","https://openalex.org/W2151554678","https://openalex.org/W2163212353","https://openalex.org/W2278519563","https://openalex.org/W2700766797","https://openalex.org/W2761700016","https://openalex.org/W2765458100","https://openalex.org/W2895269073","https://openalex.org/W2938597499","https://openalex.org/W2969209099","https://openalex.org/W2986188471","https://openalex.org/W2995903439","https://openalex.org/W2998993395","https://openalex.org/W2999553114","https://openalex.org/W3013460382","https://openalex.org/W3016665712","https://openalex.org/W3033559620","https://openalex.org/W3089087699","https://openalex.org/W3138239626","https://openalex.org/W3205154884","https://openalex.org/W4210548680","https://openalex.org/W4212993228","https://openalex.org/W4220727031","https://openalex.org/W4220803424","https://openalex.org/W4223935973","https://openalex.org/W4283715676","https://openalex.org/W4313341957","https://openalex.org/W4319082028","https://openalex.org/W4391547718","https://openalex.org/W4399027146","https://openalex.org/W4403836749"],"related_works":["https://openalex.org/W2602382373","https://openalex.org/W3003615511","https://openalex.org/W4285827128","https://openalex.org/W3198113463","https://openalex.org/W2787698406","https://openalex.org/W2961085424","https://openalex.org/W2963844355","https://openalex.org/W4306674287","https://openalex.org/W4361251046","https://openalex.org/W98577079"],"abstract_inverted_index":{"Decision":[0,10,244],"trees":[1,11,38],"in":[2,8,36,187,196,315,345,376,381,387],"machine":[3],"learning":[4],"achieved":[5],"satisfactory":[6],"performance":[7],"classification.":[9,61],"offer":[12],"the":[13,32,51,65,72,87,95,129,137,147,161,172,179,191,206,212,216,309,346,363,368],"advantage":[14],"of":[15,34,64,128,136,149,163,171,181,208,296,317,324,331,339,367,392],"handling":[16],"high-dimensional":[17],"and":[18,25,168,193,210,215,247,252,280,282,305,321,328,335,337,343,356,383],"complexly":[19],"correlated":[20],"data":[21],"through":[22],"feature":[23],"combination":[24],"selection.":[26],"Extreme":[27],"Gradient":[28,236],"Boosting":[29],"(XGBoost)":[30],"overcomes":[31],"issue":[33,148],"overfitting":[35],"decision":[37],"by":[39,71],"integrating":[40],"multiple":[41],"tree":[42],"models.":[43],"Weighted":[44],"cognitive":[45,115],"avoidance":[46,116],"particle":[47,117,138],"swarm":[48,118,139],"optimization":[49,119,254],"for":[50,58,86,123,175],"XGBoost":[52,66,88,182,209],"(WCAPSO-XGB)":[53],"model":[54,68,80,89,204,311,370],"has":[55],"been":[56],"proposed":[57,130,158,202,310,369],"credit":[59,213],"score":[60],"The":[62,82,126,157,201,302],"structure":[63],"(XGB)":[67],"is":[69,121,143],"determined":[70],"hyperparameters,":[73],"which":[74,142,189],"must":[75],"be":[76,105],"initialized":[77],"prior":[78],"to":[79,110,145,300,374,379,385],"evaluation.":[81],"optimal":[83,155],"hyperparameter":[84,124,253],"values":[85],"can":[90,104],"vary":[91],"significantly":[92],"depending":[93],"on":[94,288,398],"specific":[96],"problem":[97],"at":[98],"hand.":[99],"Manually":[100],"setting":[101],"these":[102],"hyperparameters":[103,180,207],"a":[106,133,153,197,293,390],"time-consuming":[107],"process.":[108],"Therefore,":[109],"automate":[111],"this":[112],"process,":[113],"weighted":[114],"(WCAPSO)":[120],"employed":[122],"optimization.":[125],"novelty":[127],"work":[131],"includes":[132],"modified":[134],"version":[135],"optimizer":[140],"(PSO),":[141],"used":[144],"address":[146],"getting":[150],"trapped":[151],"into":[152],"local":[154,166],"solution.":[156],"WCAPSO":[159],"restricts":[160],"movement":[162,195],"particles":[164,186],"toward":[165],"optima":[167],"enhances":[169],"exploration":[170],"search":[173,199],"space":[174],"better":[176,313],"solutions.":[177],"Here,":[178],"are":[183,219],"represented":[184],"as":[185,225,257],"WCAPSO,":[188],"defines":[190],"state":[192],"its":[194],"continuous":[198],"space.":[200],"WCAPSO-XGB":[203],"tunes":[205],"classifies":[211],"scoring,":[214],"experimental":[217,303],"results":[218,304,314],"compared":[220],"with":[221,286,292,389],"various":[222],"classifier":[223],"such":[224,256],"Random":[226,261],"Forest":[227],"(RF),":[228],"K-neighbors":[229],"(KNN),":[230],"Gaussian":[231],"Naive":[232],"Bayes":[233],"(NB),":[234],"AdaBoost,":[235],"Boosting,":[237],"Logistic":[238],"Regression":[239],"(LR),":[240],"Neural":[241],"Network":[242],"(NN),":[243],"Tree":[245],"(DT)":[246],"Linear":[248],"Discriminant":[249],"Analysis":[250],"(LDA),":[251],"methods,":[255],"Grid":[258],"Search":[259,262,278],"(GS),":[260],"(RS),":[263],"Bays":[264],"Optimization,":[265,267],"Optuna":[266],"Hybrid":[268],"Snake":[269],"Optimizer":[270],"Algorithm":[271],"(HSOA),":[272],"Exploratory":[273],"Cuckoo":[274,277],"Search,":[275],"island":[276],"(iCSPM":[279],"iCSPM2),":[281],"Improved":[283],"SSA":[284],"(ISSA)":[285],"HDPM,":[287],"four":[289],"different":[290],"datasets":[291],"varying":[294],"number":[295],"instances":[297],"from":[298,372],"small":[299],"large.":[301],"analysis":[306],"show":[307],"that":[308],"secures":[312],"accuracy":[316],"76.75%,":[318],"82.43%,":[319],"88.98%,":[320],"88.74%;":[322],"F1-score":[323],"66.52%,":[325],"89.44%,":[326],"93.53%,":[327],"90.64%;":[329],"precision":[330],"78.79%,":[332],"84.31%,":[333],"88.46%,":[334],"90.41%;":[336],"recall":[338],"64.07%,":[340],"95.24%,":[341],"99.21%,":[342],"90.87%":[344],"UCI":[347,349],"German,":[348],"Taiwan":[350],"dataset,":[351,360],"P2P":[352],"US":[353],"lending":[354],"club,":[355],"Credit":[357],"scoring":[358],"Kaggle":[359],"respectively.":[361],"Further,":[362],"overall":[364],"confidence":[365,391],"interval":[366],"ranges":[371],"82%":[373],"83%":[375],"accuracy,":[377],"73.1%":[378],"74.2%":[380],"F1-score,":[382],"71%":[384],"72%":[386],"AUC,":[388],"95%":[393],"measured":[394],"over":[395],"20":[396],"evaluations":[397],"each":[399],"dataset.":[400]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}