The choquet integral with respect to λ,-measure based on γ-support

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题名:	The choquet integral with respect to λ,-measure based on γ-support
作者:	Liu, Hsiang-Chuan;Tu, Yu-Chieh;Chen, Chin-Chun;Weng, Wei-Sheng
贡献者:	Department of Bioinformatics
关键词:	Control theory;Cybernetics;Integral equations;Learning systems;Mean square error;Robot learning;C-support;Choquet integrals;Cross validations;Fuzzy measure;Fuzzy support;Improved methods;Independent variables;Mean squares;Multicollinearity;Multiple regression models;Real datums;Regression models;Ridge regressions;V-support
日期:	2008
上传时间:	2010-04-08 12:06:10 (UTC+0)
出版者:	Asia University
摘要:	hen the multicollinearity between independent variables occurs in the multiple regression models, its performance will always be poor. The traditional improved method which is always used is the ridge regression model. Recently, the Choquet integral regression model with fuzzy measure can further be exploited to improve this situation. In this study, we found that based on different fuzzy support, the Choquet integral regression model with the same fuzzy measure may have different performances, three kinds of fuzzy supports, C-support, V-support and γ-support proposed by our work were considered. For evaluating the performances of the Choquet integral regression models with P-measure or λ-measure based on above different fuzzy supports, a real data experiment by using a 5-fold cross-validation mean square error (MSE) is conducted. Experimental result shows that the Choquet integral regression model with λ-measure based on γ-support has the best performance. © 2008 IEEE.
關聯:	Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC 6 :3602-3606
显示于类别:	[生物資訊與醫學工程學系 ] 會議論文

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