关键词: 支持向量机, 特征选择, 参数优化, 思维进化算法, 学习, 反思

Abstract: Feature selection and parameter optimization are two main methods which improve the classification performance of Support Vector Machine（SVM）. The optimization of feature selection and parameter synchronization can improve the classification accuracy. Mind Evolutionary Algorithm（MEA） is used for feature selection and SVM parameters optimization can achieve good classification results, but there are also some problems such as slow convergence speed and easy to fall into local optimum which lead to the classification accuracy could not be further improved. Aiming at this problem, an improved mind evolutionary algorithm for feature selection and SVM parameter optimization is proposed into which the “learning” and “reflection” mechanism are introduced. The algorithm learns through the information sharing among subgroups and reflects through the comparison of the fitness value. In this way it ensures the diversity of population and increases the speed of convergence, and further improves the classification accuracy. Experimental results show the effectiveness of the algorithm.

Key words: support vector machine, feature selection, parameter optimization, mind evolutionary algorithm, learning, reflection