Informative trees by visual pruning

作者：

Highlights：

• A one-step procedure of pruning and decision tree selection is provided.

• We define a new way to represent the tree structure by a dendrogram-like output.

• Our approach can be used to build up both classification and regression trees.

• We show the performance of the proposed approach using real world data sets.

摘要

•A one-step procedure of pruning and decision tree selection is provided.•We define a new way to represent the tree structure by a dendrogram-like output.•Our approach can be used to build up both classification and regression trees.•We show the performance of the proposed approach using real world data sets.

论文关键词：CART,Impurity proportional reduction,Cost-complexity pruning,Visualization,Supervised statistical learning

论文评审过程：Received 26 January 2018, Revised 11 March 2019, Accepted 12 March 2019, Available online 12 March 2019, Version of Record 16 March 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.03.018