Optimize Algorithm of Decision Tree Based on Rough Sets Hierarchical Attributes

Rough set theory is a new mathematical tool to deal with vagueness and uncertainty. And now it has been widely, applied in constructing decision tree which has no hierarchical attributes inside. However, hierarchical attributes exist generally in realistic environment, which leads that decision making has max rides. Using max rules to build decision trees can optimize decision trees and has practical values as well. So, in order to deal with hierarchical attributes in decision tree, this paper try to design an optimize algorithm of decision tree based on rough sets hierarchical attributes (ARSHA), which works by, combining the hierarchical attribute values and deleting the associated objects when max rides exist in decision table. So that the algorithm developed in this paper can abstract the simplest rule set that can cover all information for decision making. Finally, a real example is used to demonstrate its feasibility and efficiency.