Employing discrete Bayes error rate for discretization and feature selection tasks

Abstract

The tasks of discretization and feature selection are frequently used to improve classification accuracy. We use discrete approximation of Bayes error rate to perform discretization on the features. The discretization procedure targets minimization of Bayes error rate within each partition. A class-pair discriminatory measure can be defined on discretized partitions which forms the basis of the feature selection algorithm. A small value of this measure for a class-pair indicates that the class-pair in consideration is confusing and the features which distinguish them well should be chosen first. A video classification problem on a large database is considered for showing the comparison of a classifier using our discretization and feature selection tasks with SVM, neural network classifier, decision trees and K-nearest neighbor classifier.