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Towards objective data selection in bankruptcy prediction | IEEE Conference Publication | IEEE Xplore

Towards objective data selection in bankruptcy prediction


Abstract:

This paper proposes and tests a methodology for selecting features and test cases with the goal of improving medium term bankruptcy prediction accuracy in large uncontrol...Show More

Abstract:

This paper proposes and tests a methodology for selecting features and test cases with the goal of improving medium term bankruptcy prediction accuracy in large uncontrolled datasets of financial records. We propose a Genetic Programming and Neural Network based objective feature selection methodology to identify key inputs, and then use those inputs to combine multi-level Self-Organising Maps with Spectral Clustering to build clusters. Performing objective feature selection within each of those clusters, this research was able to increase out-of-sample classification accuracy from 71.3% and 69.8% on the Genetic Programming and Neural Network models respectively to 80.0% and 77.3%.
Date of Conference: 10-15 June 2012
Date Added to IEEE Xplore: 02 August 2012
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Conference Location: Brisbane, QLD, Australia

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