Cite as: Iorio C, Aria M, D'Ambrosio A & Siciliano R 2019, 'Informative trees by visual pruning', Expert Systems with Applications, vol. 127, pp. 228–240.
Open access: This article has been publisehed under the gold open access scheme and the published version is available on the Zenodo repository: https://zenodo.org/record/3267338.
Abstract: The aim of this study is to provide visual pruning and decision tree selection for classification and re- gression trees. Specifically, we introduce an unedited tree graph to be made informative for recursive tree data partitioning. A decision tree is visually selected through a dendrogram-like procedure or through au- tomatic tree-size selection. Our proposal is a one-step procedure whereby the most predictive paths are visualized. This method appears to be useful in all real world cases where tree-path interpretation is cru- cial. Experimental evaluations using real world data sets are presented. The performance was very similar to Classification and Regression Trees (CART) benchmarking methodology, showing that our method is a valid alternative to the well-known method of cost-complexity pruning.