An improved boosting based on feature selection for corporate bankruptcy prediction

Authors:
Gang Wang;Jian Ma;Shanlin Yang
Affiliations:
School of Management, Hefei University of Technology, Hefei, Anhui 230009, PR China and Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei, Anhui, ...;Department of Information Systems, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong;School of Management, Hefei University of Technology, Hefei, Anhui 230009, PR China and Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei, Anhui, ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2014

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Abstract

With the recent financial crisis and European debt crisis, corporate bankruptcy prediction has become an increasingly important issue for financial institutions. Many statistical and intelligent methods have been proposed, however, there is no overall best method has been used in predicting corporate bankruptcy. Recent studies suggest ensemble learning methods may have potential applicability in corporate bankruptcy prediction. In this paper, a new and improved Boosting, FS-Boosting, is proposed to predict corporate bankruptcy. Through injecting feature selection strategy into Boosting, FS-Booting can get better performance as base learners in FS-Boosting could get more accuracy and diversity. For the testing and illustration purposes, two real world bankruptcy datasets were selected to demonstrate the effectiveness and feasibility of FS-Boosting. Experimental results reveal that FS-Boosting could be used as an alternative method for the corporate bankruptcy prediction.