摘要： Failure-node prune(FNP)剪枝算法是在深入分析和研究人工智能机器学习中ID3算法的基础上,提出的一种新的剪枝算法。通过采用CMU的4个具有典型特征的数据库对ID3算法、Expected-error prune剪枝算法和Failure-node prune剪枝算法进行实验对比分析,结果表明,Failure-node prune剪枝算法对于不完整的、不确定的、规模大的数据集有更好的剪枝效果,具有一定的应用价值。

关键词: 机器学习, ID3算法, FNP剪枝算法, 信息熵

Abstract: This paper studies the exist prune algorithms of decision tree in machine learning of artificial intelligence, and proposes a new prune algorithm—failure-node prune algorithm. Usually, when a decision tree is pruned, the number of the tree’s nodes will decrease and the correctness will also be cut down. The failure-node prune algorithm proposed in this paper can increase the correctness while the number of the tree’s nodes is decreased when the data sets are quite deficient. Four typical databases obtained from CMU are used to do the experiments on the ID3 algorithm, the expected-error prune algorithm and the failure-node prune algorithm. In the experiments, it divides a data set into three parts, used two of them to form the training set and the third part as the testing set. The results of the experiments suggest the failure-node prune algorithm has a quite good pruning effect.

中图分类号: 

• TP18