具有模糊数值属性的双分支决策树的归纳

D. Huang, Xizhao Wang, M. Ha
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引用次数: 4

摘要

本文提出了一种基于信息熵最小化启发式的模糊数值属性算法。该算法给出了理想的信息熵划分行为。通过对非稳定切点的分析,提高了算法的学习效率,实验结果表明,决策树生成中的叶数随着level /spl alpha/的提高而减少。因此,决策树的规模和分类识别率随着level /spl alpha/的提高而提高。对于未知分类的样本数据,该算法具有较快的匹配速度。最后,以我们在医院收集的医疗记录为例,展示了所提出算法的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Induction of bi-branches decision tree with fuzzy number-value attribute
This paper presents an algorithm regarding the fuzzy number-valued attribute using the information entropy minimization heuristic. The algorithm gives us a desirable behavior of the information entropy of partitioning. The efficiency of the learning algorithm is improved by analyzing the non-stable cut point and the experiment result shows that the number of leaves in decision tree generation is reduced with the raising of level /spl alpha/. Thus, the scale of decision tree and the recognition rate of classification using the proposed algorithm are improved with the raising of level /spl alpha/. To the unknown-classified sample data, the algorithm offers a rapid matching speed. Finally, the example on medical records that we collected in a hospital shows the utility of the proposed algorithm.
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