两种模糊决策树算法的对比研究

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。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。该文主要对两种启发式算法即FuzzyID3和Min-Ambiguity算法应用于符号值属性并且类分明情况所作的分析比较。通过实验与理论分析,发现FuzzyID3算法应用于符号值属性类分明的数据库时从训练准确度、测试准确度和树的规模等方面都要优于Min-Ambiguity算法。

Comparison of Two Decision Tree Induction Algorithm

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 has symbol features and crisp classes.Because building optimal fuzzy decision tree is NP-hard,it is necessary to study the heuristics information.In this article,we compare two heuristics,i.e.FuzzyID3and Min-Ambiguity algorithms ,by using symbol and crisp data.We found ID3algorithm is better than Min -Ambiguity in training accuracy,testing accuracy and size of tree by experimental and theoretical analysis.