基于差分进化的多目标粒子群特征选择算法

Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-11-29 Cooperative journals: 《计算机应用研究》

李敏 章国豪 陈梓樑 郭志勇 胡晓敏

Abstract： Feature selection technology plays an important role in big data analysis, image processing, bioinformatics and other fields. In practical applications, the objectives of reducing the classification error rate and reducing the number of extracted features for facilitating the use of subsequent data, are often two conflicting goals. The multi-object particle swarm optimization based on crowding, mutation, dominance for feature selection (CMDPSOFS) is a kind of bi-objective optimization algorithm with the minimal number of features and classification error rate in feature-oriented selection applications. The algorithm uses three different mutation mechanisms for maintaining swarm diversity and balancing global and local search capabilities. However, the uniform variation increases the randomness of the algorithm, resulting in the generation of worse solutions, which reduces the convergence speed of the algorithm. This paper proposed an improved CMDPSOFS-II algorithm to introduce the mutation and selection operations of differential evolution algorithm into the CMDPSOFS algorithm. The experimental results show that the CMDPSOFS-II algorithm is superior to the original method in feature selection and better balances global and local search capabilities.

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