Improved Ordinal Decisions Trees Algorithms Based on Rank Entropy
CHEN Jian-Kai, WANG Xi-Zhao, GAO Xiang-Hui
Key Laboratory on Machine Learning and Computational Intelligence of Hebei Province, College of Mathematics and Computer Science, Hebei University, Baoding 071002
Abstract When the expanded attributes are selected for decision tree learning based on rank entropy, computing the rank mutual information of every single cut for each of the continuous-valued attributes is required to get the expanded attribute by comparing the values of rank mutual information. Therefore, the computational complexity is high. Aiming at this problem, cut-points are divided into stable and unstable cut-points and a mathematical model is established in this paper. The proposed model theoretically proves that the rank mutual information function achieves its maximum not at stable cut-points, but at unstable cut-points. The result means that in the algorithm only traversing the unstable cut-points is required instead of computing the values of the stable cut-points. Thus, the computational efficiency of building decision trees is greatly improved, which is confirmed by the numerical experimental results.
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2014, Vol. 27 
