Combining Rough Set-based Relevance and Redundancy for the Ranking and Selection of Nominal Features

In this paper, we propose a new method for features ranking and selection. Our approach is based on ranking nominal features in terms of their relevance to the assigned class and mutual redundancy with the other features. To calculate the relevance and redundancy, we propose to use a rough-set based approach. After performing the ranking, features filtering is carried out in a supervised way enabling the user to decide on the number of the retained features. The experiments revealed that thanks to our method, it is possible to filter out numerous features describing data while still maintaining satisfactory classification accuracy achieved by the classifier trained using the reduced dataset. The comparative experiments performed with the use of publicly available datasets proved the high efficiency and competitiveness of our approach.