Open AccessRETRACTED: To Reduce Error Rate and Improve Performance of Classification Algorithm
Author(s) -
S. Nagaparameshwara Chary,
B. Rama
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/981/2/022064
Subject(s) - computer science , classifier (uml) , word error rate , discriminative model , estimator , margin (machine learning) , bayes error rate , nonparametric statistics , a priori and a posteriori , bayes' theorem , machine learning , artificial intelligence , algorithm , feature selection , minification , naive bayes classifier , pattern recognition (psychology) , bayes classifier , mathematics , bayesian probability , statistics , support vector machine , philosophy , epistemology , programming language
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.