A comparison on choquet integral with respect to different information-based fuzzy measures

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Title:	A comparison on choquet integral with respect to different information-based fuzzy measures
Authors:	Chang, Horng-Jinh;Liu, Hsiang-Chuan;Tseng, Shang-Wen;Chang, Fengming M.
Contributors:	Department of Bioinformatics
Keywords:	Control theory;Cybernetics;Linear regression;Robot learning;C-measure;Choquet integral;Choquet integral regression model;E-measure;M-measure
Date:	2009
Issue Date:	2010-04-08 12:05:49 (UTC+0)
Publisher:	Asia University
Abstract:	In this paper, for grouped data, three kinds of the Choquet integral regression models with fuzzy measures based on joint entropy, complexity and multiple mutual information is considered. The above three fuzzy measures are called, E-measure, C-measure and M-measure, respectively. For evaluating the Choquet integral regression models with these three information-based fuzzy measures, a real grouped data experiment by using a 5-fold cross validation accuracy is conducted. The performances of the Choquet integral regression models based on these three fuzzy measures, respectively, and the traditional multiple linear regression model are compared. Experimental result shows that the Choquet integral regression model based on our proposed M-measure has the best performance and it outperforms the Choquet integral regression model based on our previous proposed C-measure. © 2009 IEEE.
Relation:	Proceedings of the 2009 International Conference on Machine Learning and Cybernetics 6 :3161-3166
Appears in Collections:	[生物資訊與醫學工程學系 ] 會議論文

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