Wrapper feature selection embedded Bagging for financial distress prediction

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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Original languageEnglish
Pages (from-to)375-380
Journal / PublicationICIC Express Letters, Part B: Applications
Issue number2
Publication statusPublished - 2013


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.

Research Area(s)

  • Bagging, Ensemble learning, Feature selection, Financial distress prediction