A Harmony Search Based Wrapper Feature Selection Method for Holistic Bangla word Recognition
This is an incremental improvement for researchers in pattern recognition, specifically for handwritten Bangla word recognition, by optimizing feature selection to enhance accuracy.
The paper tackles feature dimensionality reduction for handwritten Bangla word recognition by introducing a Harmony Search-based wrapper method, which selects 48 out of 65 features and achieves a recognition accuracy of 90.29%, outperforming previous methods like GA, PSO, and PCA.
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.