A Novel Approach of Rough Set-Based Attribute Reduction Using Fuzzy Discernibility Matrix

Rough set approach is one of effective attribute reduction (also called a feature selection) methods that can preserve the meaning of the attributes(features). However, most of existing algorithms mainly aim at information systems or decision tables with discrete values. Therefore, in this pa- per, we introduce a novel rough set-based method followed by establishing a fuzzy discernibility matrix by using dis- tance preserving strategy for attribute reduction, and only choose fisher discriminant analysis with kernels as discrim- inant criteria for testing the effectiveness of selected at- tribute subsets with relatively higher fitness values, since the proposed method is independent of post-analysis algo- rithms (predictors).Experimental results show that the clas- sifiers developed using the selected attribute subsets have better or comparable performance on all eight UCI bench- mark datasets than those obtained by all attributes. Thus, our newly developed method can, in most cases, get effec- tive attribute subsets. In addition, this method can be di- rectly incorporated into other learning algorithms, such as PCA, SVM and etc. and can also be more easily applied to many real applications, such as Web Categorization, Image recognition and etc.