An Interpolation-Based Evolutionary Algorithm for Bi-Objective Feature Selection in Classification

When aimed at minimizing both the classification error and the number of selected features, feature selection can be treated as a bi-objective optimization problem suitable for solving with multi-objective evolutionary algorithms (MOEAs). However, traditional MOEAs may encounter difficulties due to discrete optimization environments and the curse of dimensionality in the feature space, especially for high-dimensional datasets. Therefore, in this paper an interpolation-based evolutionary algorithm (termed IPEA) is proposed for tackling bi-objective feature selection in classification, where an interpolation based initialization method is designed for covering a wide range of search space and exploring the adaptively detected regions of interest. In experiments, IPEA is been compared with four state-of-the-art MOEAs in terms of two widely-used performance metrics on a list of 20 public real-world classification datasets with the dimensionality ranging from low to high. The overall empirical results suggest that IPEA generally performs the best of all tested algorithms, with significantly better search abilities and much lower computational time cost.