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A lot of search approaches have been explored for the selection of featuresin pattern classification domain in order to discover significant subset of thefeatures which produces better accuracy. In this paper, we introduced a HarmonySearch (HS) algorithm based feature selection method for feature dimensionalityreduction in handwritten Bangla word recognition problem. This algorithm hasbeen implemented to reduce the feature dimensionality of a technique describedin one of our previous papers by S. Bhowmik et al.[1]. In the said paper, a setof 65 elliptical features were computed for handwritten Bangla word recognitionpurpose and a recognition accuracy of 81.37% was achieved using Multi LayerPerceptron (MLP) classifier. In the present work, a subset containing 48features (approximately 75% of said feature vector) has been selected by HSbased wrapper feature selection method which produces an accuracy rate of90.29%. Reasonable outcomes also validates that the introduced algorithmutilizes optimal number of features while showing higher classificationaccuracies when compared to two standard evolutionary algorithms like GeneticAlgorithm (GA), Particle Swarm Optimization (PSO) and statistical featuredimensionality reduction technique like Principal Component Analysis (PCA).This confirms the suitability of HS algorithm to the holistic handwritten wordrecognition problem.

A Harmony Search Based Wrapper Feature Selection Method for Holistic Bangla word Recognition