Rough Set Approach Based on Approximation Quality in Face Recognition

摘要

The main contribution of this paper is the presentation of the rough set approach based on approximation quality that can be used in attributes reduction. Different from common works, the approach is the multi-implementation of the reduction process based on approximation quality, and the processing will end when performance criterion goal is achieved. Experiments on datasets from UCI repository result in good performance. The method is also employed in face recognition and the numerical results are presented. These results reveal that the performance of the reduced data set is almost as good as the original, whereas the amount of reduced attributes is only around 60 percent of the unreduced.