A comparative study of discretization method for HMM classifiers

Abstract

Discretization continuous feature is an important task to handle the problems with real values in machine learning. In order to construct a classifier using a discrete space, it is required for many supervised classification algorithms to perform with discretized features. In this paper, we presents the supervised classification model based on Hidden Markov Model (HMM) developed recently and we review several discretization methods reported in the litterature. We take 9 benchmarking study data set to evaluate the performance and study the effect of discretization methods on the assessment of the proposed learning algorithm. Three metrics performance including accuracy, Area under curve and squared loss are used to investigate the powerful class prediction and to show the capability of HMM classifier in presence of available data.