An improved boosting based on feature selection for corporate bankruptcy prediction

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

48 Scopus Citations
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Original languageEnglish
Pages (from-to)2353-2361
Journal / PublicationExpert Systems with Applications
Volume41
Issue number5
Early online date29 Sep 2013
Publication statusPublished - Apr 2014

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. © 2013 Elsevier Ltd. All rights reserved.

Research Area(s)

  • Boosting, Corporate bankruptcy prediction, Ensemble learning, Feature selection