An efficient Pawlak reduction algorithm based on bitmap and granular computing

Attribute reduction in rough set theory is an important feature selection method, which can apply successfully in data mining and machine leaning .etc. In this paper, a new algorithm of attribute reduction is proposed which based on bitmap and granular computing. At first, in order to reduce the research space, we need not to compute the record vectors if only the number of corresponding class vector is equal to one. Then a new heuristic information which can efficiently reduce the numbers of granular computing is proposed. That is to say, we reduce some computations which can not change the results of attribute reduction. At last, some different datasets on UCI are used to test the performance of the new algorithm. The experimental results show that the proposed algorithm is more efficient than the other relevant algorithms.