A New Approach for Improving Classification Accuracy in Predictive Discriminant Analysis

The focus of a predictive discriminant analysis is to improve classification accuracy, and to obtain statistically optimal classification accuracy or hit rate is still a challenge due to the inherent variability of most real life dataset. Improving classification accuracy is usually achieved with best subset of relevant predictors obtained by using classical variable selection methods. The goal of variable selection methods is to choose the best subset (or training sample) of relevant variables that typically reduces the complexity of a model and makes it easier to interpret, improves the classification accuracy of the model and reduces the training time. However, a statistically optimal hit rate can be achieved if the training sample meets a near optimal condition by resolving any significant differences in the variances for the groups formed by the dependent variable. This paper proposes a new approach for obtaining a near optimal training sample that will produce a statistically optimal hit rate using a modified winsorization with graphical diagnostic. In application to real life data sets, the proposed new approach was able to identify and remove legitimate contaminants in one or more predictors in the training sample, thereby resolving any significant differences in the variances for the groups formed by the dependent variable. The graphical diagnostic associated with the new approach, however, provides a useful visual tool which served as an alternative graphical test for homogeneity of variances.