| 摘要: |
| 该文提出了一种区间值属性决策树的学习算法.区间值属性的值域不同于离散情况下的无序集和连续情况下的全序集,而是一种半序集.作为ID3算法在区间值意义下的推广,算法通过一种分割信息熵的极小化来选取扩展属性.通过非平稳点分析,减少了分割信息熵的计算次数,使算法的效率得到了提高. |
| 关键词: 机器学习,归纳学习,决策树,区间值属性. |
| DOI： |
| 分类号: |
| 基金项目:本文研究得到河北省自然科学基金资助. |
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| Learning Algorithm of Decision Tree Generation for Interval-Valued Attributes |
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WANG Xi-zhao,HONG Jia-rong
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| Abstract: |
| The authors present a learning algorithm of decision tree generation for interval-valued attributes. With regard to range of value, a nominal attribute is not ordered and a continuous-valued attribute is linearly ordered, but the interval-valued attribute is partially ordered. As a generalization of ID3-algorithm on intervals, this algorithm uses minimal information entropy of partitioning to select the extended attributes. The efficiency of the algorithm is improved by analyzing unstable cut points. |
| Key words: Machine learning, induction, decision trees, interval-valued attributes. |
