On the Use of Modified Winsorization with Graphical Diagnostic for Obtaining a Statistically Optimal Classification Accuracy in Predictive Discriminant Analysis

Abstract

In predictive discriminant analysis (PDA), the classification accuracy is only statistically optimal if each group sample is normally distributed with different group means, and each predictor variance is similar between the groups. This can be achieved by accounting for homogeneity of variances between the groups using the modified winsorization with graphical diagnostic (MW-GD) method. The MW-GD method involves the identification and removal of legitimate contaminants in a training sample with the aim of obtaining a true optimal training sample that can be used to build a predictive discriminant function (PDF) that will yield a statistically optimal classification accuracy. However, the use of this method is yet to receive significant attention in PDA. An alternative statistical interpretation of the graphical diagnostic information associated with the method when confronted with the challenge of differentiating between a variable shape in the groups of the 2-D area plot remains a problem to be resolved. Therefore, this paper provides a more comprehensive analysis of the idea and concept of the MW-GD method, as well as proposed an alternative statistical interpretation of the informative graphical diagnostic associated with the method when confronted with the challenge of differentiating between a variable shape in the groups of the 2-D area plot.