Combination of Harmony Search and Linear Discriminate Analysis to Improve Classification

Abstract

An appropriate pre-processing algorithm in classification is not only of great importance with respect to classifier choice, but also would be more crucial. In this paper, a pre-processing step is proposed in order to increase accuracy of classification. The aim of this approach is finding a transformation matrix causes classes to be more discriminable by transforming data into the new space and consequently, increases the classification accuracy. This transformation matrix is computed through two methods based on linear discrimination. In the first method, we use class independent LDA to increase classification accuracy by finding a transformation that maximizes the between-class scatter and minimizes within-class scatter using a transformation matrix. Because LDA cannot obtain optimal transformation, in the second method, Harmony Search is used to increase performance of LDA. Obtained results show that utilization of these pre-processing causes increasing the accuracy of different classifiers.