关键词: 属性约简, 全局约简, 启发式算法, 局部约简, 邻域粗糙集

Abstract: Traditional criteria of attribute reduction for neighborhood decision error rate is designed for improving overall classification accuracy, it does not take the variation of accuracy of each class into consideration when reduction finding is executed. From this point of view, the concepts of local neighborhood decision error rate and local attribute reduction are introduced for improving the classification accuracy of single class. Furthermore, a heuristic algorithm to compute local neighborhood decision error rate based reduction is presented. The experimental results on 8 UCI data sets show that the local reduction can not only improve the classification accuracy of single class, but also overcome the limitation of accuracy’s decreasing for single class, which may be caused by global reduction.

Key words: attribute reduction, global reduction, heuristic algorithm, local reduction, neighborhood rough set