An Efficient Decision Tree Algorithm Using Rough Set Theory

Decision tree are commonly used for gaining information for the purpose of decision making. Decision tree starts with a root node on which it is for users to take actions. From this node, users split each node recursively according to decision tree learning algorithm. The final result is a decision tree in which each branch represents a possible scenario of decision and its outcome. Decision tree is widely used in machine learning. Rough set theory is emerging as a powerful toll for reasoning about data. Attribute reduction is one of important topics in the research on the rough set theory. In this paper, proposed a novel algorithm is presented to optimize decision tree using rough set theory and the degree of dependency in rough set theory is used to reduce condition attribute as well as to select splitting attribute. Our technique is optimized by level wise that is the condition attribute which is irrelevant in one level is deleted and further, if it is important in the next level than it can be used. By this algorithm drawback of id3 that is repetition of condition attribute is resolved and limited number of nodes are produced. Keywords: Classification, Rough Set, Decision tree, Split, Pruning.