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Non-linear principal component analysis-based hybrid classifiers: an application to bankruptcy prediction in banks

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  • Vadlamani Ravi
  • Chelimala Pramodh

Abstract

This paper presents various non-linear principal component analysis (NLPCA)-based two-phase hybrid classifiers for predicting bankruptcy in banks. The first phase of the hybrids performs dimensionality reduction using NLPCA, which is implemented as a threshold accepting trained auto associative neural network (TAAANN). By considering the non-linear principal components as new inputs, second phase is invoked. In the second phase, which is essentially a classifier, we employed threshold accepting neural network (TANN), TANN without hidden layer, threshold accepting trained logistic regression (TALR) and multi layer perceptron (MLP). The results are compared with that of MLP, radial basis function neural network and found that the proposed hybrids performed well. It was observed that the NLPCA-TANN hybrid outperformed other hybrids over all data sets studied here. Further, TALR outperformed all the hybrids over all data sets. Based on the results, we infer that the hybrid classifiers performed very well by yielding high accuracies.

Suggested Citation

  • Vadlamani Ravi & Chelimala Pramodh, 2010. "Non-linear principal component analysis-based hybrid classifiers: an application to bankruptcy prediction in banks," International Journal of Information and Decision Sciences, Inderscience Enterprises Ltd, vol. 2(1), pages 50-67.
    • Handle: RePEc:ids:ijidsc:v:2:y:2010:i:1:p:50-67
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    Cited by:

    1. Aykut Ekinci & Halil İbrahim Erdal, 2017. "Forecasting Bank Failure: Base Learners, Ensembles and Hybrid Ensembles," Computational Economics, Springer;Society for Computational Economics, vol. 49(4), pages 677-686, April.

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