To Reduce Error Rate and Improve Performance of Classification Algorithm

Abstract

In this paper, an improved method is introduced to reduce the error rate of the standard SD in the context of a two-class classification problem. The learning procedure of the improved method consists of two stages. Initially a shorter learning periodic carried out to identify an important space where all the misclassified samples are located. And the discriminative optimal criterion is computationally intractable as it involves probabilities that are not known a priori. And we have presented an algorithmic framework for feature selection based on nonparametric Bayes error minimization and our proposed framework offers sound interpretations over the existing approaches and also provides principled building blocks for establishing new algorithms. For example when weighted features are used as the search strategy as the framework reveals that the Relief algorithm greedily attempts to minimize Bayes error estimated by classification estimator. The new interpretation of Relief insightfully explains the secret behind the heuristically margin. the lower the error rate of the classifier. Consequently, the proposed improved method by its capability of achieving higher classification accuracy.