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New hybrid method for feature selection and classification using meta-heuristic algorithm in credit risk assessment

Iran Journal of Computer Science 2020年第1期3卷 1-11页

作者： Nourmohammadi-Khiarak, Jalil Feizi-Derakhshi, Mohammad-Reza Razeghi, Fatemeh Mazaheri, Samaneh Zamani-Harghalani, Yashar Moosavi-Tayebi, Rohollah Faculty of Electronics and Information Technology Warsaw University of Technology Warsaw Poland Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Faculty of Electrical and Computer Engineering Islamic Azad University Sari Iran Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Seri Kembangan Malaysia

Credit risk is a factor that arises from the failure of the party to the contract. It is one of the most important factors of risk production in banks and financial companies. Still, there is no standard set of features or indices which have been declared through all credit institutions and according to the classification of customers, they are able to do through terms of credit value. In this paper, a meta-heuristic of imperialist competitive algorithm with modified fuzzy min–max classifier (ICA-MFMCN) is offered to identify an optimal subset of features and increased through accuracy classification and scalability through assessment of credit risk. Performance of proposed ICA-MFMCN classification is approved and recognized using a real credit set that has been selected from a UCI dataset. Classification accuracy is comparable for what has been indicated through resources. The experimental which result, in obtaining new classification by utilizing the proposed are promising for future classification are selection processes in assessment of credit risk through retail, indicating that ICA-MFMCN is one of the ways which can be used to add existing data mining techniques. © The Author(s) 2019.

关键词： Credit risk Feature selection imperialist competitive algorithm and modified fuzzy min–max classifier

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