A New Method for Fast Computing Positive Region

Rough set theory is a new mathematical tool to deal with vagueness and uncertainty It has received considerable attention and has been applied in a variety of areas in recent years Positive region is one of the basic concepts in rough set theory How to compute positive region efficiently is very important for improving the performance of the relative algorithms Based on an in depth study of rough set theory, a new method for fast computing positive region is proposed and proved in this paper Furthermore, the incremental computing of positive region is analyzed Finally, the detailed descriptions of the algorithms are given In addition, their time complexities are analyzed respectively In order to test the efficiency of the algorithms, some experiments are made on the data sets in UCI （University of California, Irvine） machine learning repository The theoretical analysis and experimental results show that this new method can decrease the computational complexity effectively and it is much more efficient in comparison with those existing methods