Information Attribute Reduction Based on the Rough Set Theory

YU Bo-wen, Du Chao, Wang Zhi-guo

The genetic algorithm is used to optimize the algorithm of attribute reduction in data preprocessing, and the rough approximation precision in the rough set theory is utilized to determine the importance of information attribute. From which the decision table is constituted by selecting the attributes which have higher degree of attribute importance, and the attribute core of decision information is obtained by using the identification matrix. The initial population is constructed on the standard of the attribute core , the search area of genetic algorithm is reduced. Finally, the correction operator based on the rough approximation precision is introduced, and the algorithm is made to conduct in the correct solution space, thus the speed of attribute reduction is improved, furthermore, the optimal results of attribute reduction are obtained.