{"id":"https://openalex.org/W4390176940","doi":"https://doi.org/10.11591/ijai.v13.i1.pp917-924","title":"Explainable ensemble technique for enhancing credit risk prediction","display_name":"Explainable ensemble technique for enhancing credit risk prediction","publication_year":2023,"publication_date":"2023-12-25","ids":{"openalex":"https://openalex.org/W4390176940","doi":"https://doi.org/10.11591/ijai.v13.i1.pp917-924"},"language":"en","primary_location":{"id":"doi:10.11591/ijai.v13.i1.pp917-924","doi":"https://doi.org/10.11591/ijai.v13.i1.pp917-924","is_oa":true,"landing_page_url":"http://dx.doi.org/10.11591/ijai.v13.i1.pp917-924","pdf_url":"https://ijai.iaescore.com/index.php/IJAI/article/download/23791/13894","source":{"id":"https://openalex.org/S2764408626","display_name":"IAES International Journal of Artificial Intelligence","issn_l":"2089-4872","issn":["2089-4872","2252-8938"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","jufo-1","poland-20","russia-white-list-2"],"host_organization":"https://openalex.org/P4310315009","host_organization_name":"Institute of Advanced Engineering and Science (IAES)","host_organization_lineage":["https://openalex.org/P4310315009"],"host_organization_lineage_names":["Institute of Advanced Engineering and Science (IAES)"],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IAES International Journal of Artificial Intelligence (IJ-AI)","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ijai.iaescore.com/index.php/IJAI/article/download/23791/13894","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046226499","display_name":"N Pavitha","orcid":"https://orcid.org/0000-0002-0577-8722","observed_orcids":["https://orcid.org/0000-0002-0577-8722"]},"institutions":[{"id":"https://openalex.org/I4210088227","display_name":"MIT World Peace University","ror":"https://ror.org/004ymxd45","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210088227"]},{"id":"https://openalex.org/I4387152961","display_name":"Vishwakarma University","ror":"https://ror.org/016zwj547","country_code":"IN","type":"education","lineage":["https://openalex.org/I4387152961"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Pavitha Nooji","raw_affiliation_strings":["MIT World Peace University and Vishwakarma University"],"raw_orcid":"https://orcid.org/0000-0002-0577-8722","affiliations":[{"raw_affiliation_string":"MIT World Peace University and Vishwakarma University","institution_ids":["https://openalex.org/I4210088227","https://openalex.org/I4387152961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033392289","display_name":"Shounak Rushikesh Sugave","orcid":"https://orcid.org/0000-0002-2643-8751","observed_orcids":["https://orcid.org/0000-0002-2643-8751"]},"institutions":[{"id":"https://openalex.org/I4210088227","display_name":"MIT World Peace University","ror":"https://ror.org/004ymxd45","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210088227"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shounak Sugave","raw_affiliation_strings":["Dr. Vishwanath Karad MIT World Peace University"],"raw_orcid":"https://orcid.org/0000-0002-2643-8751","affiliations":[{"raw_affiliation_string":"Dr. Vishwanath Karad MIT World Peace University","institution_ids":["https://openalex.org/I4210088227"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3292,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.53067572,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":"13","issue":"1","first_page":"917","last_page":"917"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.5619999766349792,"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":0.5619999766349792,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.4165000021457672,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.006000000052154064,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/credit-risk-assessment","display_name":"credit risk assessment","score":1.0},{"id":"https://openalex.org/keywords/explainable-ensemble-learning","display_name":"explainable ensemble learning","score":0.8360000252723694},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"ensemble learning","score":0.7289999723434448},{"id":"https://openalex.org/keywords/model-interpretability","display_name":"model interpretability","score":0.8730000257492065},{"id":"https://openalex.org/keywords/feature-importance","display_name":"feature importance","score":0.9810000061988831},{"id":"https://openalex.org/keywords/decision-visualization","display_name":"decision visualization","score":0.8320000171661377},{"id":"https://openalex.org/keywords/credit-decisions","display_name":"credit decision-making","score":0.921999990940094},{"id":"https://openalex.org/keywords/financial-institutions","display_name":"financial institutions","score":0.6230000257492065}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8966624140739441},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7490255236625671},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.5759224891662598},{"id":"https://openalex.org/C178350159","wikidata":"https://www.wikidata.org/wiki/Q162714","display_name":"Credit risk","level":2,"score":0.5716701745986938},{"id":"https://openalex.org/C2780233690","wikidata":"https://www.wikidata.org/wiki/Q535347","display_name":"Transparency (behavior)","level":2,"score":0.5712387561798096},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.562762975692749},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.473458468914032},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47225895524024963},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.45982033014297485},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4445362687110901},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.4150159955024719},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.3949206471443176},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.11591/ijai.v13.i1.pp917-924","doi":"https://doi.org/10.11591/ijai.v13.i1.pp917-924","is_oa":true,"landing_page_url":"http://dx.doi.org/10.11591/ijai.v13.i1.pp917-924","pdf_url":"https://ijai.iaescore.com/index.php/IJAI/article/download/23791/13894","source":{"id":"https://openalex.org/S2764408626","display_name":"IAES International Journal of Artificial Intelligence","issn_l":"2089-4872","issn":["2089-4872","2252-8938"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","jufo-1","poland-20","russia-white-list-2"],"host_organization":"https://openalex.org/P4310315009","host_organization_name":"Institute of Advanced Engineering and Science (IAES)","host_organization_lineage":["https://openalex.org/P4310315009"],"host_organization_lineage_names":["Institute of Advanced Engineering and Science (IAES)"],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IAES International Journal of Artificial Intelligence (IJ-AI)","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.11591/ijai.v13.i1.pp917-924","doi":"https://doi.org/10.11591/ijai.v13.i1.pp917-924","is_oa":true,"landing_page_url":"http://dx.doi.org/10.11591/ijai.v13.i1.pp917-924","pdf_url":"https://ijai.iaescore.com/index.php/IJAI/article/download/23791/13894","source":{"id":"https://openalex.org/S2764408626","display_name":"IAES International Journal of Artificial Intelligence","issn_l":"2089-4872","issn":["2089-4872","2252-8938"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","jufo-1","poland-20","russia-white-list-2"],"host_organization":"https://openalex.org/P4310315009","host_organization_name":"Institute of Advanced Engineering and Science (IAES)","host_organization_lineage":["https://openalex.org/P4310315009"],"host_organization_lineage_names":["Institute of Advanced Engineering and Science (IAES)"],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IAES International Journal of Artificial Intelligence (IJ-AI)","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4390176940.pdf","grobid_xml":"https://content.openalex.org/works/W4390176940.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1534477342","https://openalex.org/W2131816657","https://openalex.org/W2415158095","https://openalex.org/W2594475271","https://openalex.org/W2895142658","https://openalex.org/W2972441196","https://openalex.org/W2975495759","https://openalex.org/W2995523160","https://openalex.org/W3000463950","https://openalex.org/W3014290757","https://openalex.org/W3082900329","https://openalex.org/W3088815947","https://openalex.org/W3148119887","https://openalex.org/W3172070743","https://openalex.org/W3203259065","https://openalex.org/W4251708881","https://openalex.org/W4285588387","https://openalex.org/W4293093009","https://openalex.org/W4307950795","https://openalex.org/W4307952117","https://openalex.org/W4309635040","https://openalex.org/W4311157760","https://openalex.org/W4311409325","https://openalex.org/W4313159534","https://openalex.org/W4367315749","https://openalex.org/W4378979236","https://openalex.org/W4385243690","https://openalex.org/W4387008354"],"related_works":["https://openalex.org/W7216000974","https://openalex.org/W4402811762","https://openalex.org/W4413448977","https://openalex.org/W4414622073","https://openalex.org/W4401633972","https://openalex.org/W4415314893","https://openalex.org/W4410178473","https://openalex.org/W7154419766","https://openalex.org/W7160610659","https://openalex.org/W4411757663"],"abstract_inverted_index":{"Credit":[0],"risk":[1,54,152],"prediction":[2,55,153],"is":[3,65],"a":[4],"critical":[5],"task":[6],"in":[7,26,149,159],"financial":[8,16,132,160],"institutions":[9,133],"that":[10,72,98,124],"can":[11,130],"impact":[12],"lending":[13,137],"decisions":[14],"and":[15,42,89,108,154,157],"stability.":[17],"While":[18],"machine":[19],"learning":[20],"(ML)":[21],"models":[22,35,71,107],"have":[23],"shown":[24],"promise":[25],"accurately":[27],"predicting":[28],"credit":[29,53,127,151],"risk,":[30,128],"the":[31,47,79,85,91,99,116,122,140,143],"complexity":[32],"of":[33,145],"these":[34],"often":[36],"makes":[37],"them":[38],"difficult":[39],"to":[40,51,83,126],"interpret":[41],"explain.":[43],"The":[44],"paper":[45],"proposes":[46],"explainable":[48,101,146],"ensemble":[49,63,102,147],"method":[50],"improve":[52],"while":[56],"maintaining":[57],"interpretability.":[58],"In":[59,77],"this":[60],"study,":[61],"an":[62],"model":[64,80,103,118],"built":[66],"by":[67],"combining":[68],"multiple":[69],"base":[70,106],"uses":[73],"different":[74],"ML":[75],"algorithms.":[76],"addition,":[78],"interpretation":[81],"techniques":[82],"identify":[84],"most":[86],"important":[87],"features":[88],"visualize":[90],"model's":[92],"decision-making":[93],"process.":[94],"Experimental":[95],"results":[96],"demonstrate":[97],"proposed":[100,117],"outperforms":[104],"individual":[105],"achieves":[109],"high":[110],"accuracy":[111],"with":[112],"low":[113],"loss.":[114],"Additionally,":[115],"provides":[119],"insights":[120],"into":[121],"factors":[123],"contribute":[125],"which":[129],"help":[131],"make":[134],"more":[135],"informed":[136],"decisions.":[138],"Overall,":[139],"study":[141],"highlights":[142],"potential":[144],"methods":[148],"enhancing":[150],"promoting":[155],"transparency":[156],"trust":[158],"decision-making.":[161]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-10-31T12:09:11.096373","created_date":"2023-12-25T00:00:00"}
