Volume 6 Number 7 (Jul. 2011)
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JSW 2011 Vol.6(7): 1368-1373 ISSN: 1796-217X
doi: 10.4304/jsw.6.7.1368-1373

An Empirical Study on Class Probability Estimates in Decision Tree Learning

Liangxiao Jiang1, Chaoqun Li2
1Department of Computer Science, China University of Geosciences, Wuhan, Hubei 430074, China
2Department 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.

Index Terms—decision tree learning; probability estimation tree; class probability estimation; classification; ranking.

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Cite: Liangxiao Jiang, Chaoqun Li, "An Empirical Study on Class Probability Estimates in Decision Tree Learning," Journal of Software vol. 6, no. 7, pp. 1368-1373, 2011.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly (2006-2019); Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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