because building optimal fuzzy decision tree is np-hard, it is necessary to study the heuristics 由于构建最优的模糊决策树是np-hard,因此,针对启发式算法的研究是非常必要的。
there are some questions that need be deeply researched . for example, the material application of dynamic fuzzy decision tree, the operation of decision rules etc 尽管如此,本文的工作还很基础,今后还有许多工作需做进一步研究,如动态模糊决策树的具体应用、规则的提取等。
but, fuzzy decision tree induction is an important way for learning from examples with fuzzy representation . it is a special case of fuzzy decision tree induction extracting rules from the data, which have symbol features and crisp classes 而模糊决策树归纳是从具有模糊表示的示例中学习规则的一种重要方法,从符号值属性类分明的数据中提取规则可视为模糊决策树归纳的一种特殊情况。
but, fuzzy decision tree induction is an important way for learning from examples with fuzzy representation . it is a special case of fuzzy decision tree induction extracting rules from the data, which have symbol features and crisp classes 而模糊决策树归纳是从具有模糊表示的示例中学习规则的一种重要方法,从符号值属性类分明的数据中提取规则可视为模糊决策树归纳的一种特殊情况。
(2 ) the reasonable describing about dynamic fuzzy decision tree from attributes treatment to building the tree and then pruning tree, and it provides a certain extent stated theory foundation for ulteriorly researching dynamic fuzzy decision tree and establishes a main concept frame of dynamic fuzzy decision tree (2)对动态模糊决策树从属性处理到构建以及剪枝给出了合理的描述,形成了动态模糊决策树的基本概念框架。