Soft fuzzy rough sets for robust feature evaluation and selection

The fuzzy dependency function proposed in the fuzzy rough set model is widely employed in feature evaluation and attribute reduction. It is shown that this function is not robust to noisy information in this paper. As datasets in real-world applications are usually contaminated by noise, robustness of data analysis models is very important in practice. In this work, we develop a new model of fuzzy rough sets, called soft fuzzy rough sets, which can reduce the influence of noise. We discuss the properties of the model and construct a new dependence function from the model. Then we use the function to evaluate and select features. The presented experimental results show the effectiveness of the new model.