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• W3020290450 abstract "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." @default.
• W3020290450 created "2020-05-01" @default.
• W3020290450 creator A5017541508 @default.
• W3020290450 creator A5027224656 @default.
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• W3020290450 date "2020-04-27" @default.
• W3020290450 modified "2023-10-14" @default.
• W3020290450 title "A VNS-EDA Algorithm-Based Feature Selection for Credit Risk Classification" @default.
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• W3020290450 doi "https://doi.org/10.1155/2020/4515480" @default.
• W3020290450 hasPublicationYear "2020" @default.
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