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

Vadlamani Ravi and Chelimala Pramodh

International Journal of Information and Decision Sciences, 2010, vol. 2, issue 1, 50-67

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.

Keywords: NLPCA; non-linear principal component analysis; threshold accepting trained neural networks; bankruptcy prediction; banking; threshold accepting trained auto associative neural networks; threshold accepting trained logistic regression; two-phase hybrid classifiers; dimensionality reduction; multi-layer perceptrons; radial basis function neural networks; decision science; information science. (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (2)

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