Multi-surrogate assisted binary particle swarm optimization algorithm and its application for feature selection

Abstract

The evolutionary algorithms (EAs) have been shown favorable performance for feature selection. However, a large number of evaluations are required through the EAs. Thus, they will be inappropriate to optimize feature selection when the size of data set is large. In this paper, we propose a multi-surrogate assisted binary particle swarm optimization, denoted as MS-assisted DBPSO. Two surrogate models are trained, which are utilized to approximate the fitness values of the individuals in two sub-populations, respectively. After that, a new population will be generated by the communication between the two sub-populations. Furthermore, dynamic transfer function is proposed in this paper to balance global and local search aiming to find optimal solution with limited computational resource. The experimental results on binary benchmark functions and the feature selection in the UCI data sets demonstrate that our proposed method is efficient on reducing running time and prediction error.