A Bi-Search Evolutionary Algorithm for High-Dimensional Bi-Objective Feature Selection

Abstract

High dimensionality often challenges the efficiency and accuracy of a classifier, while evolutionary feature selection is an effective method for data preprocessing and dimensionality reduction. However, with the exponential expansion of search space along with the increase of features, traditional evolutionary feature selection methods could still find it difficult to search for optimal or near optimal solutions in the large-scale search space. To overcome the above issue, in this paper, we propose a bi-search evolutionary algorithm (termed BSEA) for tackling high-dimensional feature selection in classification, with two contradictory optimizing objectives (i.e., minimizing both selected features and classification errors). In BSEA, a bi-search evolutionary mode combining the forward and backward searching tasks is adopted to enhance the search ability in the large-scale search space; in addition, an adaptive feature analysis mechanism is also designed to the explore promising features for efficiently reproducing more diverse offspring. In the experiments, BSEA is comprehensively compared with 9 most recent or classic state-of-the-art MOEAs on a series of 11 high-dimensional datasets with no less than 2000 features. The empirical results suggest that BSEA generally performs the best on most of the datasets in terms of all performance metrics, along with high computational efficiency, while each of its essential components can take positive effect on boosting the search ability and together make the best contribution.