doi: 10.4304/jsw.6.7.1368-1373
An Empirical Study on Class Probability Estimates in Decision Tree Learning
- 1 Department of Computer Science, China University of Geosciences, Wuhan, Hubei 430074, China
- 2 Department of Mathematics, China University of Geosciences, Wuhan, Hubei 430074, China
Abstract
Decision tree is one of the most effective and widely used models for classification and ranking and has received a great deal of attention from researchers in the domain of data mining and machine learning. A critical problem in decision tree learning is how to estimate the classmembership probabilities from decision trees. In this paper, we firstly survey all kinds of class probability estimation methods, mainly include the maximum-likelihood estimate, the Laplace estimate, the m-estimate, the similarity-weighted estimate, the naive Bayes-based estimate, and so on. Then, we provide an empirical study on the classification and ranking performance of the resulting decision trees using different class probability estimation methods. The experimental results based on a large number of UCI data sets verify our conclusions.
Keywords
- decision
- tree
- learning;
- probability
- esti
How to Cite
Liangxiao Jiang and Chaoqun Li, "An Empirical Study on Class Probability Estimates in Decision Tree Learning," Journal of Software, vol. 6, no. 7, pp. 1368-1373, 2011. https://doi.org/ 10.4304/jsw.6.7.1368-1373
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).