Feature selection for high-dimensional multi-category data using PLS-based local recursive feature elimination

This paper focuses on high-dimensional and ultrahigh dimensional multi-category problems and presents a feature selection framework based on local recursive feature elimination (Local-RFE). Using this analytical framework, we propose a new feature selection algorithm, PLS-based local-RFE (LRFE-PLS). In order to compare the effectiveness of the proposed methodology, we also present PLS-based Global-RFE which takes all categories into consideration simultaneously. The advantage of the proposed algorithms lies in the fact that PLS-based feature ranking can quickly delete irrelevant features and RFE can concurrently remove some redundant features. As a result, the selected feature subset is more compact. In this paper the proposed algorithms are compared to some state-of-the-art methods using multiple datasets. Experimental results show that the proposed algorithms are competitive and work effectively for high-dimensional multi-category data. Statistical tests of significance show that LRFE-PLS algorithm has better performance. The proposed algorithms can be effectively applied not only to microarray data analysis but also to image recognition and financial data analysis.