Hybrid Pruning Algorithm
In this paper we develop a new post-pruning algorithm. This new pruning algorithm uses two or more post-pruning algorithms to prune a decision tree that has been built on training set by different orders, and the best tree is selected based either on separate test set accuracy or cross-validations from trees coming from result of the above step. The algorithm is theoretically based on occams razor that is a simpler model is chosen if two models have the same performance on the training set An experiment is implemented on three databases in UCI machine learning repository and the new algorithm is employed to compares with two well-known post-pruning algorithms. The results show that the hybrid pruning algorithm effectively reduces the complexity of decision trees without sacrificing accuracy.
decision trees hybrid pruning algorithm overfitting decision tree simplification occams razor
Du Xiangran Wang Xizhao Wan Yuanyuan
Key Lab. of Machine Learning and Computational Intelligence, College of Mathematics and Computer Sci College of Journalism and Communication, Hebei University, Baoding 071002, China
国际会议
重庆
英文
30-33
2009-12-25（万方平台首次上网日期，不代表论文的发表时间）