Integration of case-based forecasting, neural network, and discriminant analysis for bankruptcy prediction

摘要

Recently, it has been an issue of interest how to integrate classification models to increase the prediction performance. This paper suggests a new structured model with multiple stages. It consists of four phases (training, test, adjustment, and prediction), and three types of input data (training, testing, and generalization). The integrated model is applied for bankruptcy prediction. A statistical model, discriminant analysis and two artificial intelligence models, neural network and case-based forecasting, are used in this study. The integration approach produces higher prediction accuracy than individual models.