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Feature subset selection approach based on fuzzy rough set for high-dimensional data

Feature subset selection, as an important processing step to knowledge discovery and machine learning, is effective method in reducing irrelevant and or redundant features, compressing repeated data, and improving classification accuracy. Rough set theory is an important tool to select feature subset from high-dimensional data. In this work, feature subset selection based on fuzzy rough set is introduced, and the efficient measure of feature significance is designed. Based on the fuzzy rough set model, a quick feature subset selection approach is presented, which can efficiently identify relevant features as well as redundancy among all features. In addition, the KNN-based classifier based on the proposed approach is constructed. The experimental results show that the proposed feature subset selection approach achieves better classification on UCI datasets.