Heuristic Algorithm for Reduction of Attributes Based on Rough Set Theory

Attribute reduction is one of the key problems in the knowledge discovery. Based on the rough set theory, a new operator is constructed to effectively achieve the minimal relative reduction of attribute in the decision table. Regarding the significance of attributes defined from the viewpoint of information theory as heuristic information and applying the breadth-first search strategy, a new heuristic algorithm for reduction of attribute is proposed. Acquiring optimal relative reduction by descending approach to core of attribute from original set of conditional attribute and combining with operator. Finally, the experimental results show that this algorithm was effective in attributes reduction of decision tables.