Robust Feature Selection Based on Fuzzy Rough Sets with Representative Sample

Abstract

Fuzzy rough set theory is not only an objective mathematical tool to deal with incomplete and uncertain information but also a powerful computing paradigm to realize feature selection. However, the existing fuzzy rough set models are sensitive to noise in feature selection. To solve this problem, a novel fuzzy rough set model that is robust to noise is studied in this paper, which expands the research of fuzzy rough set theory and broadens the application of feature selection. In this study, we propose a fuzzy rough set model with representative sample (RS-FRS), and it deals better with noise. Firstly, the fuzzy membership of the sample is defined, and it is added into the construction of RS-FRS model, which could increase the upper and lower approximation of RS-FRS and reduce the influence of the noise samples. The proposed model considers the fuzziness of the sample membership degree, and it can approximate other subsets of the domain space with the fuzzy equivalent approximation space more precisely. Furthermore, RS-FRS model does not need to set parameters for the model in advance, which helps reduce the model complexity and human intervention effectively. At the same time, we also give a careful study to the related properties of RS-FRS model, and a robust feature selection based on RS-FRS with sample pair selection is designed. Extensive experiments are given to illustrate the robustness and effectiveness of the proposed model.