JSW 2010 Vol.5(11): 1195-1199 ISSN: 1796-217X
doi: 10.4304/jsw.5.11.1195-1199
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.
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.
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: Quarterly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKI, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsweditorialoffice@gmail.com
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