A Comparative Analysis of Rule Simplification and Pruning Fuzzy Decision Trees

Decision tree induction learns the implied rules from the training set,and then uses the learned rules to predict for unseen instances.However,the crisp decision trees often suffer from overfitting the training set in real-world induction tasks.So the pruning decision tree methods are necessary in the process of building crisp decision tree to improve performance.Fuzzy decision tree induction is an extension of crisp decision tree induction and is more close to the way of human thinking.In this paper,a comparative study is made among fuzzy decision tree algorithm,the simplified rules,and fuzzy simplified rules,fuzzy decision tree and fuzzy pre-pruning methods,with the aim of understanding their theoretical foundations,their performance and the strengths and weaknesses of their formulation.The empirical results show that fuzzy decision tree is superior to crisp simplified rules.The fuzzy pre-pruning decision tree can build a good tree even without simplified rules method.