Volume 5 Number 11 (Nov. 2010)
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JSW 2010 Vol.5(11): 1195-1199 ISSN: 1796-217X
doi: 10.4304/jsw.5.11.1195-1199

Induction of a Novel Hybrid Decision Forest Model based on Information Theory

Limin Wang, Xuebai Zang, Peijuan Xu
College of Computer Science and Technology, JiLin University, ChangChun 130012, People’s Republic of China.br />
Abstract—For the task of classification, the quality of rule set is usually evaluated as a whole rather than evaluating the quality of a single rule. The present investigation proposes a hybrid classifier named FDF. By redefining information gain from the general sense of Information theory, rule sets are built and combined to be decision forest by down-top learning strategy. The finial decision tree nodes contain univariate splits as regular decision trees, but the leaves contain Naive Bayes. Empirical studies on a set of natural domains show that FDF has clear advantages with respect to the probabilistic performance.

Index Terms—rule set, decision forest, Information theory.

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Cite: Limin Wang, Xuebai Zang, Peijuan Xu, "Induction of a Novel Hybrid Decision Forest Model based on Information Theory," Journal of Software vol. 5, no. 11, pp. 1195-1199, 2010.

General Information

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