We apply rough set theory to obtain knowledge from the construction of a decision tree. Decision trees are widely used in machine learning. A variety of methods for making decision trees have been developed. Our algorithm, which compares the core attributes of objects and builds decision trees based on those attributes, represents a new type of tree construction. Experiments show that the new algorithm can help to extract more meaningful and accurate rules than other algorithms. © Springer-Verlag Berlin Heidelberg 2007 Rough set, Decision Tee, Core.
CITATION STYLE
Han, S. W., & Kim, J. Y. (2007). Rough set-based decision tree construction algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4705 LNCS, pp. 710–720). Springer Verlag. https://doi.org/10.1007/978-3-540-74472-6_58
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