Improved RBM‐based feature extraction for credit risk assessment with high dimensionality

Author:

Zhu Jianxin12,Wu Xiong12,Yu Lean123ORCID,Ji Jun4

Affiliation:

1. School of Economics and Management Harbin Engineering University Harbin 150001 People's Republic of China

2. Key Laboratory of Big Data and Business Intelligence Technology (Harbin Engineering University) Ministry of Industry and Information Technology Harbin 150001 People's Republic of China

3. Business School Sichuan University Chengdu 610065 People's Republic of China

4. China Development Bank Heilongjiang Branch Harbin 150000 People's Republic of China

Abstract

AbstractTo address the high‐dimensional issues in credit risk assessment, an improved multilayer restricted Boltzmann machine (RBM) based feature extraction method is proposed. In the improved multilayer RBM methodology, the reconstruction error method is first applied to ensure the number of RBM layers to construct an optimal model and then the weighted pruning approach is used to remove redundant and irrelevant traits. For verification purposes, two real‐world credit datasets are employed to demonstrate the effectiveness of the proposed multilayer RBM methodology. The experimental results reveal that a significant improvement in credit classification performance can be obtained by the improved multilayer RBM methodology. This indicates the improved multilayer RBM model proposed in this paper can be used as a promising tool to solve the high‐dimensionality issues in credit risk evaluation.

Publisher

Wiley

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