Scalable Feature Subset Selection With Fuzzy Rough Sets and Fuzzy Min–Max Neural Network in Hybrid Decision System

Selecting a compact and relevant feature subset for hybrid decision systems is a key task in decision-making. Fuzzy rough set (FRS) is an effective method for this purpose, but its space complexity can be a hindrance when dealing with large data sets. A scalable FRS-based feature subset selection framework, FDM-FMFRS, was proposed earlier. This framework integrates FRS with fuzzy min–max neural network (FMNN) as a preprocessing step to handle large datasets. FMNN model transforms object space into hyperbox space. However, its implementation requires all input variables to be numeric. One way to handle categorical data is to replace them with numerical values, but this method may define an unsuitable metric for the categories. This article presents an extended version of FDM-FMFRS, called Hybrid-FDM-FMFRS, which is applicable to both numeric and categorical variables by incorporating set-valued data for categorical attributes. A weighted set is presented to represent the frequency distribution of distinct categories within the hyperbox, helping to create set-valued data for categorical attributes and providing the proposed approach greater flexibility and broader applications on definite space. A comprehensive comparative analysis is conducted on hybrid benchmark datasets, and it is established that the proposed algorithm obtained relevant reduct while preserving satisfactory classification accuracy in much less computational time. In addition, the proposed approach retains the scalability benefits of FDM-FMRS, allowing it to handle large datasets where the compared algorithms fail to compute reducts.