Multi-objective feature selection based on artificial bee colony: An acceleration approach with variable sample size

Due to the need to repeatedly call a classifier to evaluate individuals in the population, existing evolutionary feature selection algorithms have the disadvantage of high computational cost. In view of it, this paper studies a multi-objective feature selection framework based on sample reduction strategy and evolutionary algorithm, significantly reducing the computational cost of algorithm without affecting optimal results. In the framework, a selection strategy of representative samples, called K-means clustering based differential selection, and a ladder-like sample utilization strategy are proposed to reduce the size of samples used in the evolutionary process. Moreover, a fast multi-objective evolutionary feature selection algorithm, called FMABC-FS, is proposed by embedding an improved artificial bee colony algorithm based on the particle update model into the framework. By applying FMABC-FS to several typical UCI datasets, and comparing with three multi-objective feature selection algorithms, experimental results show that the proposed variable sample size strategy is more suitable to FMABC-FS, and FMABC-FS can obtain better feature subsets with much less running time than those comparison algorithms.