The GSA Algorithm at Two Methods of Feature Selection and Weighting Features to Improve the Recognition Rate of Persian Handwrite Digits with Fuzzy Classifier

In this paper, using Gravitational search algorithm or GSA recognition rate of Persian handwritten digits can be improved. Two methods have been proposed to improve the recognition rate. in the first method, with using of version binary of Gravitational search algorithm or BGSA, we choose optimal features among the overall features extracted. Finding the best feature sets from entire extracted features, not only the number of features and computational burden will be reduced but also recognition rate will be significantly improved. Also In this paper, real version of GSA or RGSA has been used in different way (secondary methods) to improve recognition rate. In this method, instead of choosing some of the features, one random weight has been assigned to each feature. Indeed, feature vector has been multiplied in weight vector to obtain a new feature vector. This Weight vector is obtained with RGSA. After several iterations, RGSA algorithm determines Weight set of features so that Classification accuracy increases. In this paper, the fuzzy classifier is used for classification. Fitness function in BGSA and RGSA algorithms is the number of fuzzy classifier errors and the aim is to make this value minimum. The obtained results confirmed that these algorithms have proper performance. Keywords Increase the recognition rate, feature selection, binary Gravitational search algorithm (BGSA), Real Gravitational search algorithm (RGSA), Weight vector of features, fuzzy classifier and recognition of Persian handwritten digits. 1