Local feature selection based on artificial immune system for classification

Conventional feature selection algorithms select a global feature subset for the entire sample space. In contrast, in this paper we propose an efficient filter local feature selection algorithm based on artificial immune system, which assigns a locally relevant feature subset for each neighboring region of the sample space. This algorithm introduces a clonal selection algorithm to explore the search space for the optimal feature subsets, and adopts local clustering idea as an evaluation criterion that maximizes the inter-class distance and minimizes the intra-class distance in the small region of each sample. Experimental results on a wide variety of synthetic and UCI datasets demonstrates that our proposed method achieves better performance than both state-of-the-art global feature selection algorithms and local feature selection algorithms. In addition, a main parameter analysis of the proposed method is carried out. (C) 2019 Elsevier B.V. All rights reserved.