{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:20:03Z","timestamp":1760235603125,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,8,30]],"date-time":"2021-08-30T00:00:00Z","timestamp":1630281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["JP19H04100"],"award-info":[{"award-number":["JP19H04100"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Feature selection is crucial to the credit-scoring process, allowing for the removal of irrelevant variables with low predictive power. Conventional credit-scoring techniques treat this as a separate process wherein features are selected based on improving a single statistical measure, such as accuracy; however, recent research has focused on meaningful business parameters such as profit. More than one factor may be important to the selection process, making multi-objective optimization methods a necessity. However, the comparative performance of multi-objective methods has been known to vary depending on the test problem and specific implementation. This research employed a recent hybrid non-dominated sorting binary Grasshopper Optimization Algorithm and compared its performance on multi-objective feature selection for credit scoring to that of two popular benchmark algorithms in this space. Further comparison is made to determine the impact of changing the profit-maximizing base classifiers on algorithm performance. Experiments demonstrate that, of the base classifiers used, the neural network classifier improved the profit-based measure and minimized the mean number of features in the population the most. Additionally, the NSBGOA algorithm gave relatively smaller hypervolumes and increased computational time across all base classifiers, while giving the highest mean objective values for the solutions. It is clear that the base classifier has a significant impact on the results of multi-objective optimization. Therefore, careful consideration should be made of the base classifier to use in the scenarios.<\/jats:p>","DOI":"10.3390\/a14090260","type":"journal-article","created":{"date-parts":[[2021,8,30]],"date-time":"2021-08-30T11:01:37Z","timestamp":1630321297000},"page":"260","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Comparison of Profit-Based Multi-Objective Approaches for Feature Selection in Credit Scoring"],"prefix":"10.3390","volume":"14","author":[{"given":"Naomi","family":"Simumba","sequence":"first","affiliation":[{"name":"Graduate School of System Design and Management, Keio University, Yokohama 223-8526, Japan"}]},{"given":"Suguru","family":"Okami","sequence":"additional","affiliation":[{"name":"Graduate School of System Design and Management, Keio University, Yokohama 223-8526, Japan"}]},{"given":"Akira","family":"Kodaka","sequence":"additional","affiliation":[{"name":"Graduate School of System Design and Management, Keio University, Yokohama 223-8526, Japan"}]},{"given":"Naohiko","family":"Kohtake","sequence":"additional","affiliation":[{"name":"Graduate School of System Design and Management, Keio University, Yokohama 223-8526, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Thomas, L.C., Edelman, B.D., and Crook, N.J. (2002). Credit Scoring and Its Applications, Society for Applied and Industrial Mathematics.","DOI":"10.1137\/1.9780898718317"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Guyon, I., Gunn, S., Nikravesh, M., and Zadeh, L.A. (2006). Feature Extraction, Foundations and Applications, Springer.","DOI":"10.1007\/978-3-540-35488-8"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"113766","DOI":"10.1016\/j.eswa.2020.113766","article-title":"Enhancing credit scoring with alternative data","volume":"163","author":"Djeundje","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1016\/j.asoc.2015.05.058","article-title":"Profit-based feature selection using support vector machines\u2014General framework and an application for customer retention","volume":"35","author":"Maldonado","year":"2015","journal-title":"Appl. Soft Comput. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.dss.2017.10.007","article-title":"Integrated framework for profit-based feature selection and SVM classification in credit scoring","volume":"104","author":"Maldonado","year":"2017","journal-title":"Decis. Support Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.9790\/3021-031020114","article-title":"Review of Multi-criteria Optimization Methods\u2014Theory and Applications","volume":"3","author":"Odu","year":"2013","journal-title":"IOSR J. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.dss.2019.03.011","article-title":"A multi-objective approach for profit-driven feature selection in credit scoring","volume":"120","author":"Kozodoi","year":"2019","journal-title":"Decis. Support Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1007\/s11047-018-9685-y","article-title":"A tutorial on multiobjective optimization: Fundamentals and evolutionary methods","volume":"17","author":"Emmerich","year":"2018","journal-title":"Nat. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., and Murata, T. (2007, January 5\u20138). Evolutionary Multi-Criterion Optimization. Proceedings of the 4th International Conference, EMO 2007, Proceedings 13, Matsushima, Japan.","DOI":"10.1007\/978-3-540-70928-2"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.eswa.2018.09.015","article-title":"Binary grasshopper optimisation algorithm approaches for feature selection problems","volume":"117","author":"Mafarja","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_11","unstructured":"Hichem, H., Elkamel, M., Rafik, M., Mesaaoud, M.T., and Ouahiba, C. (2019). A new binary grasshopper optimization algorithm for feature selection problem. J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"76333","DOI":"10.1109\/ACCESS.2020.2987057","article-title":"Filter-Based Multi-Objective Feature Selection Using NSGA III and Cuckoo Optimization Algorithm","volume":"8","author":"Usman","year":"2020","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Simumba, N., Okami, S., Kodaka, A., and Kohtake, N. (2021). Hybrid Many Objective Metaheuristics for Feature Selection Based on Stakeholder Requirements in Credit Scoring with Alternative Data No Title, Unpublished manuscript, under review.","DOI":"10.1016\/j.dss.2021.113714"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ishibuchi, H., Imada, R., Setoguchi, Y., and Nojima, Y. (2016, January 16\u201321). Performance Comparison of NSGA-II and NSGA-III on Various Many-Objective Test Problems. Proceedings of the 2016 IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada.","DOI":"10.1109\/CEC.2016.7744174"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1016\/j.ejor.2017.02.037","article-title":"Cost-based feature selection for Support Vector Machines: An application in credit scoring","volume":"261","author":"Maldonado","year":"2017","journal-title":"Eur. J. Oper. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.dss.2016.06.014","article-title":"The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending","volume":"89","year":"2016","journal-title":"Decis. Support Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Verbraken, T., Member, S., Verbeke, W., and Baesens, B. (2013). A Novel Profit Maximizing Metric for Measuring Classification Performance of Customer Churn Prediction Models. IEEE Trans. Knowl. Data Eng., 25.","DOI":"10.1109\/TKDE.2012.50"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.ejor.2014.04.001","article-title":"Development and application of consumer credit scoring models using profit-based classification measures","volume":"238","author":"Verbraken","year":"2014","journal-title":"Eur. J. Oper. Res."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bonev, B., Escolano, F., and Cazorla, M. (2008). Feature selection, mutual information, and the classification of high-dimensional patterns. Pattern Anal. Appl., 11.","DOI":"10.1007\/s10044-008-0107-0"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1111\/j.1467-9868.2011.00771.x","article-title":"Regression shrinkage and selection via the lasso: A retrospective","volume":"73","author":"Tibshirani","year":"2011","journal-title":"J. R. Stat. Soc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1016\/j.engappai.2012.10.005","article-title":"Engineering Applications of Artificial Intelligence","volume":"26","author":"Han","year":"2013","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3053","DOI":"10.1007\/s00500-017-2953-4","article-title":"Sparse multi-criteria optimization classifier for credit risk evaluation","volume":"23","author":"Zhang","year":"2019","journal-title":"Soft Comput."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Xue, B., Cervante, L., Shang, L., and Zhang, M. (2012). A Particle Swarm Optimisation Based Multi-Objective Filter Approach to Feature Selection for Classification. Proceedings of the PRICAI 2012: Trends in Artificial Intelligence. PRICAI 2012. Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/978-3-642-32695-0_59"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.dss.2019.01.002","article-title":"Two-stage consumer credit risk modelling using heterogeneous ensemble learning","volume":"118","author":"Papouskova","year":"2019","journal-title":"Decis. Support Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Emmanouilidis, C., Hunter, A., Macintyre, J., and Cox, C. (1999, January 7\u201310). Selecting Features in Neurofuzzy Modelling by Multiobjective Genetic Algorithms. Proceedings of the ICANN\u201999. 9th International Conference on Artificial Neural Networks, Edinburgh, UK.","DOI":"10.1049\/cp:19991201"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1080\/09540091.2012.737765","article-title":"A Multi-Objective Particle Swarm Optimisation for Filter Based Feature Selection in Classification Problems","volume":"24","author":"Xue","year":"2012","journal-title":"Conn. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1023\/B:ANOR.0000039513.99038.c6","article-title":"Pareto Ant Colony Optimization: A Metaheuristic Approach to Multiobjective Portfolio Selection","volume":"131","author":"Doerner","year":"2004","journal-title":"Ann. Oper. Res."},{"key":"ref_28","unstructured":"Wagner, T., Beume, N., and Naujoks, B. (2007, January 5\u20138). Pareto-, Aggregation-, and Indicator-Based Methods in Many-Objective Optimization. Proceedings of the 4th International Conference, EMO 2007, Matsushima, Japan."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Deb, K., and Jain, H. (July, January 28). Handling many-objective problems using an improved NSGA-II procedure. Proceedings of the 2012 IEEE Congress on Evolutionary Computation (CEC\u201912), Krak\u00f3w, Poland.","DOI":"10.1109\/CEC.2012.6256519"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/BF01442131","article-title":"Pareto Optimality in Multiobjective Problems","volume":"4","author":"Censor","year":"1977","journal-title":"Appl. Math. Optim."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, B., Li, J., Tang, K., and Yao, X. (2015). Many-objective evolutionary algorithms: A survey. ACM Comput. Surv., 48.","DOI":"10.1145\/2792984"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.advengsoft.2017.01.004","article-title":"Advances in Engineering Software Grasshopper Optimisation Algorithm: Theory and application","volume":"105","author":"Saremi","year":"2017","journal-title":"Adv. Eng. Softw."},{"key":"ref_33","unstructured":"Mays, E., and Nuetzel, P. (2004). Credit Scoring for Risk Managers: The Handbook for Lenders, South-Western Publishing. Ch. Scorecard Monitoring Reports."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.ejor.2020.11.016","article-title":"Performance indicators in multiobjective optimization","volume":"292","author":"Audet","year":"2021","journal-title":"Eur. J. Oper. Res."},{"key":"ref_35","unstructured":"Dua, D., and Graff, C. (2019). German Credit Dataset, University of California, School of Information and Computer Science."},{"key":"ref_36","unstructured":"Khan, S., Asjad, M., and Ahmad, A. (2015). Review of Modern Optimization Techniques. Int. J. Eng. Tech. Res."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/9\/260\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:56:02Z","timestamp":1760165762000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/9\/260"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,30]]},"references-count":36,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["a14090260"],"URL":"https:\/\/doi.org\/10.3390\/a14090260","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2021,8,30]]}}}