Metric Based Attribute Reduction in Incomplete Decision Tables

摘要

Metric technique has recently been applied to solve such data mining problems as classification, clustering, feature selection, decision tree construction. In this paper, we apply metric technique to solve a attribute reduction problem of incomplete decision tables in rough set theory. We generalize Liang entropy in incomplete information systems and investigate its properties. Based on the generalized Liang entropy, we establish a metric between coverings and study its properties for attribute reduction. Consequently, we propose a metric based attribute reduction method in incomplete decision tables and perform experiments on UCI data sets. The experimental results show that metric technique is an effective method for attribute reduction in incomplete decision tables.