As no single-discriminant method outperforms other discriminant methods under all circumstances, decision-makers may solve a classification problem using several discriminant methods and examine their performance for classification purposes in the training sample. Based on this performance, better classification methods might be adopted and poor methods might be avoided. However, which single-discriminant method is best to predict the classification of new observations is still not clear, especially when some methods offer a similar classification performance in the training sample. In this paper, we present a method that combines several discriminant methods to predict the classification of new observations. Simulation experiments are run to test this combining technique.
