期刊
APPLIED SOFT COMPUTING
卷 87, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.asoc.2019.105989
关键词
Local feature selection; Artificial immune system; Clonal selection algorithm; Dimensionality reduction; Classification
资金
- National Key Research and Development Program of China [2016YFB0800600]
- Natural Science Foundation of China [U1736212, 61572334, 61872254, 61872255]
- Sichuan Province Key Research and Development Project of China [2018GZ0183]
- Fundamental Research Funds for the central Universities, China
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.
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