区间值属性决策树学习算法  

Learning Algorithm of Decision Tree Generation for Interval Valued Attributes

作　　者：王熙照[1] 洪家荣[1] 

机构地区：[1]哈尔滨工业大学计算机科学系

出　　处：《软件学报》1998年第8期637-640,共4页Journal of Software

基　　金：河北省自然科学基金

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．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.

关 键 词：机器学习 归纳学习 决策树 区间值属性 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]