A relative uncertainty measure for fuzzy rough feature selection

Abstract

Uncertainty measure is an important tool for data analysis. In practical applications, the collected data are subject to different probability distributions. This requires that the uncertainty measure has generalization performance. Fuzzy rough set (FRS) theory is a popular mathematical tool for uncertainty measure, but the theory does not work well for some data distributions. For example, when the class density difference of the data set is large, FRS theory cannot effectively evaluate the classification uncertainty of samples. In this study, we combine the relative measure with the lower approximation of FRSs to propose a relative uncertainty measure which can address the above-mentioned problem. Furthermore, a fuzzy rough feature selection algorithm is designed, and it is mainly used to test the effectiveness and efficiency of the proposed measure. Experimental results demonstrate that the proposed feature selection algorithm has good performance. It indirectly proves that the relative uncertainty measure is effective and efficient in classification tasks.