Abstract
In this paper, we discuss an approach of heuristic attribute reduction from large-scale data based on rough set theory and statistical methods. Attribute reduction is one of the most important research topics in the aspect of reasoning from data based on rough set. However, because of high computational complexity of attribute reduction, it is severe to directly apply attribute reduction methods to data with numerous samples and attributes. To solve this issue, in this paper, we introduce a hybrid method of a statistical approach of attribute reduction that was originally proposed by Bazan et al. and a heuristic attribute reduction from a decision tables with numerous attributes that was proposed by the authors.
