Metaheuristics for Feature Selection in Handwritten Digit Recognition

Recognition of handwritten digits by computers is a common research topic in the pattern recognition area and has application in several domains. Many techniques can be applied in order to maximize the recognition performance, such as image preprocessing, feature extraction, feature selection and classification stages. This paper focuses on the assessment of three swarm intelligence optimization algorithms for feature selection optimization, called Binary Fish School Search (BFSS), Advanced Binary Ant Colony Optimization (ABACO) and Binary Particle Swarm Optimization (BPSO), for the recognition of handwritten digits. These meta-heuristics were applied to the well-known handwritten digit database MNIST, preprocessed with the CDF 9/7 Wavelet Transform. We used support vector machine (SVM) for the classification task. A considerable reduction in the number of features used for digit classification on the MNIST database with a small loss in the classification rates was observed.