Selecting the appropriate fuzzy membership functions based on user-demand in fuzzy decision-theoretic rough set model

Fuzzy rough sets are generalization of rough sets to handle fuzziness and uncertainty existed in data. The decision rough set model (DTRS) is the kind of probabilistic rough set model with proper cost functions. We combine fuzzy sets with decision-theoretic rough set (DTRS) theory and propose a new equation by employing the fuzzy membership functions to change the posterior probability calculating method containing in the expected losses expression of the DTRS model. Thus we can derive the new decision rules. With different user demands, varied thresholds in DTRS are firstly set to decide with probability an object can be classified to the positive region. For different fuzzy membership functions, we propose the method to select the appropriate one based on user-demand in our new fuzzy rough set model. Experiments on different datasets show that different membership functions do result in different classification performances when threshold has been set in advance. With our method it can give guidelines on appropriate fuzzy membership functions selection for improving classification accuracy.