INTEGRATION OF CASE-BASED FORECASTING, NEURAL-NETWORK, AND DISCRIMINANT-ANALYSIS FOR BANKRUPTCY PREDICTION

Recently, it has been an issue of interest how to integrate classifica tion models to increase the prediction performance. This paper suggest s a new structured model with multiple stages. It consists of four pha ses (training, test, adjustment, and prediction), and three types of i nput data (training, testing, and generalization). The integrated mode l is applied for bankruptcy prediction. A statistical model, discrimin ant analysis and two artificial intelligence models, neural network an d case-based forecasting, are used in this study. The integration appr oach produces higher prediction accuracy than individual models. Copyr ight (C) 1996 Elsevier Science Ltd