Improvements to the classification performance of RDA

By means of an extensive simulation it was shown that the new model selection procedure performs better than the original one and one of the control parameters of regularized discriminant analysis is allowed to take on negative values, which leads to an improved performance in certain situations.

Abstract

Abstract Regularized discriminant analysis has proven to be a most effective classifier for problems where traditional classifiers fail because of a lack of sufficient training samples, as is often the case in highdimensional settings. However, it has been shown that the model selection procedure of regularized discriminant analysis, determining the degree of regularization, has some deficiencies associated with it. We propose a modified model selection procedure base on a new appreciation function. By means of an extensive simulation it was shown that the new model selection procedure performs better than the original one. We also propose that one of the control parameters of regularized discriminant analysis be allowed to take on negative values. This extension leads to an improved performance in certain situations. The results are confirmed using two chemical data sets.