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Combining Feature Selection and Classification Using LASSO-Based MCO Classifier for Credit Risk Evaluation

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Abstract

Credit risk evaluation is a difficult task to predict default probabilities and deduce risk classification, and many classification methods and techniques have already been applied in predicting credit risk. In this paper, in view of the significant limitations of feature reduction and weak interpretability of the multi-criteria optimization classifier (MCOC), an improved LASSO-based MCOC (LASSO-MCOC) for simultaneous classification and feature selection is proposed and the corresponding algorithm is constructed. Based on the four real-world credit risk datasets, the LASSO-MCOC with linear and RBF kernels are tested and compared with the SMCOC proposed by Zhang et al. (2019) and six basic classification methods including logistic regression, multilayer perceptron, support vector machines, Naïve Bayes, k-nearest neighbors and random forest. The experimental and statistically comparative analysis results show that the LASSO-MCOC we proposed is more effective for credit risk assessment with better performance in accuracy, efficiency, and interpretability than that of other classifiers and can be extended to other real-world applications.

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Funding

This study has been partially supported by the National Natural Science Foundation of China under Grant 61877061, and in part by the Major Program of Natural Science Foundation of the Higher Education Institutions of Jiangsu Province under Grant 22KJA520003 and Yantai School Land Integration Development Project under Grant 2021PT02.

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Authors and Affiliations

  1. Department of Science and Technology, Ludong University, Yantai, 264025, Shandong, China

    **ufang Li

  2. College of Information Engineering, Nan**g University of Finance and Economics, Nan**g, 210023, China

    Zhiwang Zhang

  3. School of Information and Electrical Engineering, Ludong University, Yantai, 264025, Shandong, China

    Lingyun Li & Hui Pan

Authors
  1. **ufang Li
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  2. Zhiwang Zhang
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  3. Lingyun Li
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  4. Hui Pan
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Contributions

All authors contributed to the study scheme and design. Algorithm design and optimization were performed by ZZ. Data collection, experiment and analysis were performed by XL, LL and HP. The first draft of the manuscript was written by XL and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Zhiwang Zhang.

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The authors declare that they have no known competing financial interests or personal relationships which could have appeared to influence the work reported in this paper.

Financial interests

All authors once participated in the development of bank management projects. Zhiwang Zhang began to research the credit risk evaluation earlier.

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Li, X., Zhang, Z., Li, L. et al. Combining Feature Selection and Classification Using LASSO-Based MCO Classifier for Credit Risk Evaluation. Comput Econ (2024). https://doi.org/10.1007/s10614-023-10535-8

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