期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 36, 期 3, 页码 4517-4522出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2008.05.020
关键词
Fuzzy information gain; Fuzzy entropy; Classification problems; Feature weights; Membership grades
类别
资金
- National Science Council, Republic of China [NSC 95-2221-E-011-116-MY2]
In this paper, we present a new method for handling classification problems using a new fuzzy information gain measure. Based on the proposed fuzzy information gain measure, we propose an algorithm for constructing membership functions, calculating the class degree of each subset of training instances with respect to each class and Calculating the fuzzy entropy of each subset of training instances. Based on the constructed membership function of each fuzzy set of each feature, the obtained class degree of each subset of training instances with respect to each class and the obtained fuzzy entropy of each subset of training instances, we propose an evaluating function for classifying testing instances. The proposed method gets higher average classification accuracy rates than the methods presented in [John, G. H., & Langley. P. (1995). Estimating continuous distributions in Bayesian classifiers. In Proceedings of the 11th conference oil uncertainty in artificial intelligence, Montreal, Canada (pp. 338-345): Platt, J. C. (1999). Using analytic QP and sparseness to speed training of support vector machines. In Proceedings of the 13th annual conference on neural information processing systems, Denver, Colorado (pp. 557-563); Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Francisco: Morgan Kaufmann]. (c) 2008 Elsevier Ltd. All rights reserved.
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