Wrapper feature selection embedded Bagging for financial distress prediction

作     者：Wang, Gang Yang, Shanlin Ma, Jian 

作者机构：School of Management Hefei University of Technology No. 193 Tunxi Road Hefei 230009 China Ministry of Education Key Laboratory of Process Optimization and Intelligent Decision-Making Hefei University of Technology No. 193 Tunxi Road Hefei 230009 China Department of Information Systems City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong 

出 版 物：《ICIC Express Letters, Part B: Applications》 (ICIC Express Lett Part B Appl.)

年 卷 期：2013年第4卷第2期

页      面：375-380页

核心收录：

主　　题：Forecasting 

摘      要：The prediction of financial distress for financial institutions has been extensively researched for a long time. Latest studies have shown that such ensemble techniques have performed better than single AI technique in financial distress prediction. In this paper a new wrapper feature selection embedded Bagging, WFS-Bagging, is proposed to predict financial distress. WFS-Bagging utilizes the feature selection, e.g., wrapper feature selection, to enhance the accuracy and diversity of base learners. For the testing and illustration purposes, two real world financial distress data sets are selected to demonstrate the effectiveness and feasibility of proposed method. Experimental results reveal that WFS-Bagging can be used as an alternative technique for the financial distress prediction. © 2013 ICIC International.