Robust fuzzy rough approximations with kNN granules for semi-supervised feature selection

Abstract

Fuzzy rough set theory has attracted much attention because of its successful application in uncertainty measurement. To improve the efficiency and robustness of uncertainty measure using the theory, robust fuzzy rough approximations have become popular research. In this work, it first designs a novel uncertainty measure based on kNN granules. By combining the relative distance, the method is effectively applicable to the datasets with large density variation. Using the measure a robust FRS model is studied and simply denoted KNN-FRS model. The new model takes a compromise between the maximum lower approximation degree and the minimum classification loss in computing fuzzy rough approximations, which reduces the sensitivity of the classical FRS model to noise. Besides, some properties of KNN-FRS model are summarized. Next, KNN-FRS model is used to design a feature selection algorithm which is tested to validate the feasibility of the KNN-FRS model. And then the KNN-FRS model is applied to design a semi-supervised feature selection algorithm which selects features while labeling samples and the labeling method is designed based on the proposed uncertainty measure. Some experiments are conducted to validate the KNN-FRS model and the designed algorithms. The results illustrate the proposed KNN-FRS model is feasible, effective and robust. And the designed semi-supervised feature selection algorithm performs excellent performance.