A VNS-EDA Algorithm-Based Feature Selection for Credit Risk Classification
Joint Authors
Li, Zhongfei
Chen, Wei
Guo, Jinchao
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-27
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Many quantitative credit scoring models have been developed for credit risk assessment.
Irrelevant and redundant features may deteriorate the performance of credit risk classification.
Feature selection with metaheuristic techniques can be applied to excavate the most significant features.
However, metaheuristic techniques suffer from various issues such as being trapped in local optimum and premature convergence.
Therefore, in this article, a hybrid variable neighborhood search and estimation of distribution technique with the elitist population strategy is proposed to identify the optimal feature subset.
Variable neighborhood search with the elitist population strategy is used to direct its local searching in order to optimize the ergodicity, avoid premature convergence, and jump out of the local optimum in the searching process.
The probabilistic model attempts to capture the probability distribution of the promising solutions which are biased towards the global optimum.
The proposed technique has been tested on both publicly available credit datasets and a real-world credit dataset in China.
Experimental analysis demonstrates that it outperforms existing techniques in large-scale credit datasets with high dimensionality, making it well suited for feature selection in credit risk classification.
American Psychological Association (APA)
Chen, Wei& Li, Zhongfei& Guo, Jinchao. 2020. A VNS-EDA Algorithm-Based Feature Selection for Credit Risk Classification. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1195217
Modern Language Association (MLA)
Chen, Wei…[et al.]. A VNS-EDA Algorithm-Based Feature Selection for Credit Risk Classification. Mathematical Problems in Engineering No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1195217
American Medical Association (AMA)
Chen, Wei& Li, Zhongfei& Guo, Jinchao. A VNS-EDA Algorithm-Based Feature Selection for Credit Risk Classification. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1195217
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1195217