  A lot of search approaches have been explored for the selection of features
in pattern classification domain in order to discover significant subset of the
features which produces better accuracy. In this paper, we introduced a Harmony
Search (HS) algorithm based feature selection method for feature dimensionality
reduction in handwritten Bangla word recognition problem. This algorithm has
been implemented to reduce the feature dimensionality of a technique described
in one of our previous papers by S. Bhowmik et al.[1]. In the said paper, a set
of 65 elliptical features were computed for handwritten Bangla word recognition
purpose and a recognition accuracy of 81.37% was achieved using Multi Layer
Perceptron (MLP) classifier. In the present work, a subset containing 48
features (approximately 75% of said feature vector) has been selected by HS
based wrapper feature selection method which produces an accuracy rate of
90.29%. Reasonable outcomes also validates that the introduced algorithm
utilizes optimal number of features while showing higher classification
accuracies when compared to two standard evolutionary algorithms like Genetic
Algorithm (GA), Particle Swarm Optimization (PSO) and statistical feature
dimensionality reduction technique like Principal Component Analysis (PCA).
This confirms the suitability of HS algorithm to the holistic handwritten word
recognition problem.
