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Feature selection is a common dimension reduction approach for processing high-dimensional big data, but it often involves multiple conflicting feature subsets evaluation objectives which are difficult to balance . To reach a compromise among various feature subset evaluations in feature selection and optimize the performance of subset, this paper proposed a subset evaluation multi-objective optimization based feature selection framework and focused on the application of multi-objective particle swarm optimization( MOPSO) in feature subset evaluation. The framework used sparsity, classification ability and information loss to design multi-objective optimization functions. Then it optimized the weight vectors of the features based on multi-objective optimization algorithm, and selected the " knee" of Pareto solution set as optimal vector. Finally, the framework realized feature selection based on weight vector ranking. This paper designed experiments to compare the performance of MOPSO based feature selection( FS_MOPSO) with four classical methods. The results on several standard data sets show that, FS_MOPSO shows higher classification accuracy in low dimensional space while ensuring less information loss. [ABSTRACT FROM AUTHOR]

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) was a kind of bi-objective optimization algorithm with the minimal number of features and classification error rate in feature-oriented selection applications. The algorithm used three different mutation mechanisms for maintaining swarm diversity and balancing global and local search capabilities. However, the uniform variation increased the randomness of the algorithm, resulting in the generation of worse solutions, which reduced 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. [ABSTRACT FROM AUTHOR]