A statistical approach to growing a reliable honest tree

Authors:
Carmela Cappelli;Francesco Mola;Roberta Siciliano
Affiliations:
Department of Mathematics and Statistics, Naples University Federico II, Via Cinthia Monte S Angelo, 80126 Naples, Italy;Department of Economics, University of Cagliari, Italy;Department of Mathematics and Statistics, Naples University Federico II, Via Cinthia Monte S Angelo, 80126 Naples, Italy
Venue:
Computational Statistics & Data Analysis
Year:
2002

Citing 8
Cited 1

Learning decision rules in noisy domains

Proceedings of Expert Systems '86, The 6Th Annual Technical Conference on Research and development in expert systems III
Simplifying decision trees

International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Bagging predictors

Machine Learning
Multivariate data analysis and modeling through classification and regression trees

Computational Statistics & Data Analysis
A fast splitting procedure for classification trees

Statistics and Computing
An Empirical Comparison of Pruning Methods for Decision Tree Induction

Machine Learning
The CN2 Induction Algorithm

Machine Learning
On Estimating Probabilities in Tree Pruning

EWSL '91 Proceedings of the European Working Session on Machine Learning

Comparison of logistic regression model and classification tree: An application to postpartum depression data

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.03

Visualization

Abstract

The introduction of a third stage in tree growing approach is suggested. The aim is to find a honest tree, that is, a tree which is not only understandable and accurate, but also statistically reliable. Testing procedures for both classification and regression trees are introduced. These procedures guide the search for those parts in tree structures which are statistically significant.