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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