ROUGH SET REDUCTION OF ATTRIBUTES AND THEIR DOMAINS FOR NEURAL NETWORKS

This paper presents an empirical study of the use of the rough set app roach to reduction of data for a neural network classifying objects de scribed by quantitative and qualitative attributes. Two kinds of reduc tion are considered: reduction of the set of attributes and reduction of the domains of attributes. Computational tests were performed with five data sets having different character, for original and two reduce d representations of data. The learning time acceleration due to data reduction is up to 4.72 times. The resulting increase of misclassifica tion error does not exceed 11.06%. These promising results let us clai m that the rough set approach is a useful tool for preprocessing of da ta for neural networks.