ROUGH SET-BASED DECISION TREE USING A CORE ATTRIBUTE
Sang-Wook Han, Jae-Yearn Kim · International Journal of Information Technology & Decision Making · 2008
Decision trees are widely used in machine learning and artificial intelligence. In this paper, we extend previous research and present a new decision tree classification algorithm that uses a rough set theory to produce classification rules. Our algorithm is based on core attributes and on comparing the values of attributes between objects. Our experiments compared the performance of the Iterative Dichotomiser 3 (ID3) algorithm, C4.5, and the proposed decision tree algorithm to demonstrate its accuracy and ability to simplify rules.