Article ID: | iaor20062704 |
Country: | United Kingdom |
Volume: | 32 |
Issue: | 10 |
Start Page Number: | 2561 |
End Page Number: | 2582 |
Publication Date: | Oct 2005 |
Journal: | Computers and Operations Research |
Authors: | Pendharkar Parag C. |
Keywords: | forecasting: applications, neural networks, statistics: empirical |
We propose a threshold-varying artificial neural network (TV-ANN) approach for solving the binary classification problem. Using a set of simulated and real-world data set for bankruptcy prediction, we illustrate that the proposed TV-ANN fares well, both for training and holdout samples, when compared to the traditional backpropagation artificial neural network (ANN) and the statistical linear discriminant analysis. The performance comparisons of TV-ANN with a genetic algorithm-based ANN and a classification tree approach C4.5 resulted in mixed results.