An analytical study of the classification of highly skewed data

This article proposes a discriminant function and an algorithm to analyze the data addressing the situation, where the data are positively skewed. The performance of the suggested algorithm based on the suggested discriminant function (LNDF) has been compared with the conventional linear discriminant function (LDF) and quadratic discriminant function (QDF) as well as with the nonparametric support vector machine (SVM) and the Random Forests (RFs) classifiers, using real and simulated datasets. A maximum reduction of approximately 81% in the error rates as compared to QDF for ten-variate data was noted. The overall results are indicative of better performance of the proposed discriminant function under certain circumstances.