Optimization of a subset of apple features based on modified particle swarm algorithm

Reducing dimension processing is needed in feature samples because the repeated and secondary features would reduce the classification ability and increase computation complexity. In this paper, a feature selection method, named MPSO (Modified Particle Swarm Optimization), is proposed. The original group velocity of a particle swarm was changed into two separate and parallel particle swarm velocity, which was effectively and quickly applied to the feature extraction of the optimum samples on the basis of Discrete Binary PSO. Then the least squares support vector machine classifier is used to verify the feasibility of this method. The experimental results show that, compared with the method in the literature, the iteration times in this method are only 17 times in average, while the iteration times in the literature are 23 times; the selected features and the average recognition accuracy after feature selection are slightly better than the ones in the method in the literature. Therefore, the proposed method is feasible and effective.