{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T20:13:00Z","timestamp":1770235980137,"version":"3.49.0"},"reference-count":35,"publisher":"Emerald","issue":"7","license":[{"start":{"date-parts":[[2014,7,29]],"date-time":"2014-07-29T00:00:00Z","timestamp":1406592000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,7,29]]},"abstract":"<jats:sec>\n               <jats:title content-type=\"abstract-heading\">Purpose<\/jats:title>\n               <jats:p> \u2013 Credit ratings have become one of the primary references for financial institutions to assess credit risk. Conventional credit rating approaches mainly concentrated on two-class classification (i.e. good or bad credit), which lacks adequate precision to perform credit risk evaluations in practice. In addition, most of previous researches directly focussed on employing various data mining techniques, but rare studies discussed the influence of data preprocessing before classifier construction. The paper aims to discuss these issues. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title>\n               <jats:p> \u2013 This study considers nine-class classification (i.e. nine credit risk level) to credit rating prediction. For the development of more accurate classifiers, the paper adopts two-stage analysis, which integrates multiple data preprocessing and supervised learning techniques. Specifically, the first stage applies feature selection, data clustering, and data resampling methods to preprocess the data, and then the second stage utilizes several classification techniques and classifier ensembles to construct prediction models. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Findings<\/jats:title>\n               <jats:p> \u2013 The results show that Bagging-DT with data resampling method achieves excellent accuracy (82.96 percent), indicating that the proposed two-stage prediction model is better than conventional one-stage models. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title>\n               <jats:p> \u2013 Practical implication of this study can lower credit rating expenses and also allow corporations to gain credit rating information instantly.<\/jats:p>\n            <\/jats:sec>","DOI":"10.1108\/k-10-2013-0218","type":"journal-article","created":{"date-parts":[[2014,7,29]],"date-time":"2014-07-29T13:38:19Z","timestamp":1406641099000},"page":"1098-1113","source":"Crossref","is-referenced-by-count":24,"title":["Two-stage credit rating prediction using machine learning techniques"],"prefix":"10.1108","volume":"43","author":[{"given":"Hsu-Che","family":"Wu","sequence":"first","affiliation":[]},{"given":"Ya-Han","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Yen-Hao","family":"Huang","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2020123022372259200_b1","doi-asserted-by":"crossref","unstructured":"Angelini, E.\n               , \n                  di Tollo, G.\n                and \n                  Roli, A.\n                (2008), \u201cA neural network approach for credit risk evaluation\u201d, Quarterly Review of Economics & Finance, Vol. 48 No. 4, pp. 733-755.","DOI":"10.1016\/j.qref.2007.04.001"},{"key":"key2020123022372259200_b2","doi-asserted-by":"crossref","unstructured":"Baesens, B.\n               , \n                  Gestel, T.V.\n               , \n                  Viaene, S.\n               , \n                  Stepanova, M.\n               , \n                  Suykens, J.\n                and \n                  Vanthienen, J.\n                (2003), \u201cBenchmarking state-of-the-art classification algorithms for credit scoring\u201d, The Journal of the Operational Research Society, Vol. 54 No. 6, pp. 627-635.","DOI":"10.1057\/palgrave.jors.2601545"},{"key":"key2020123022372259200_b3","doi-asserted-by":"crossref","unstructured":"Belkaoui, A.\n                (1980), \u201cIndustrial bond ratings: a new look\u201d, Financial Management, Vol. 9 No. 3, pp. 44-51.","DOI":"10.2307\/3664892"},{"key":"key2020123022372259200_b4","doi-asserted-by":"crossref","unstructured":"Bennell, J.A.\n               , \n                  Crabbe, D.\n               , \n                  Thomas, S.\n                and \n                  Gwilym, O.A.\n                (2006), \u201cModelling sovereign credit ratings: neural networks versus ordered probit\u201d, Expert Systems with Applications, Vol. 30 No. 3, pp. 415-425.","DOI":"10.1016\/j.eswa.2005.10.002"},{"key":"key2020123022372259200_b5","doi-asserted-by":"crossref","unstructured":"Caruana, R.\n                and \n                  Niculescu-Mizil, A.\n                (2006), \u201cAn empirical comparison of supervised learning algorithms\u201d, Proceedings of the 23rd International Conference on Machine Learning, pp. 161-168.","DOI":"10.1145\/1143844.1143865"},{"key":"key2020123022372259200_b6","doi-asserted-by":"crossref","unstructured":"Chen, L.-H.\n                and \n                  Chiou, T.-W.\n                (1999), \u201cA fuzzy credit-rating approach for commercial loans: a Taiwan case\u201d, Omega, Vol. 27 No. 4, pp. 407-419.","DOI":"10.1016\/S0305-0483(98)00051-6"},{"key":"key2020123022372259200_b7","doi-asserted-by":"crossref","unstructured":"Chen, W.-H.\n                and \n                  Shih, J.-Y.\n                (2006), \u201cA study of Taiwan's issuer credit rating systems using support vector machines\u201d, Expert Systems with Applications, Vol. 30 No. 3, pp. 427-435.","DOI":"10.1016\/j.eswa.2005.10.003"},{"key":"key2020123022372259200_b8","doi-asserted-by":"crossref","unstructured":"Cheng, H.\n               , \n                  Lu, Y.-C.\n                and \n                  Sheu, C.\n                (2009), \u201cAn ontology-based business intelligence application in a financial knowledge management system\u201d, Expert Systems with Applications, Vol. 36 No. 2, pp. 3614-3622.","DOI":"10.1016\/j.eswa.2008.02.047"},{"key":"key2020123022372259200_b9","doi-asserted-by":"crossref","unstructured":"Desai, V.S.\n               , \n                  Crook, J.N.\n                and \n                  Overstreet, G.A. 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