Choquet integral with respect to sigma-fuzzy measure

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Title:	Choquet integral with respect to sigma-fuzzy measure
Authors:	Liu, Hsiang-Chuan;Wu, Der-Bang;Jheng, Yu-Du;Chen, Chin-Chun;Chien, Maw-Fa;Sheu, Tian-Wei
Contributors:	Department of Bioinformatics
Keywords:	Linear regression;Mean square error;Mechatronics;Choquet integral;Closed form;Cross validation;Forecasting models;Fuzzy measures;Multiple linear regression models;Regression model;Ridge regression
Date:	2009
Issue Date:	2010-04-08 12:05:57 (UTC+0)
Publisher:	Asia University
Abstract:	Both the well known fuzzy measures, λ-measure and P-measure, have only one formulaic solution, the former is not a closed form, and the latter not sensitive enough. In this paper, A novel multivalent fuzzy measure with infinitely many solutions, called σ-measure, is proposed. This new measure can be considered as an extension of the P- measure andλ- measure, For evaluating the Choquet integral regression models with our proposed fuzzy measure and other different ones, a real data experiment by using a 5-fold cross-validation mean square error (MSE) is conducted. The performances of Choquet integral regression models with fuzzy measure based on σ-measure, λ-measure and P-measure, respectively, a ridge regression model and a multiple linear regression model are compared. Experimental result shows that the Choquet integral regression models with respect to σ-measure based on γ-support outperforms other forecasting models. ©2009 IEEE.
Relation:	2009 IEEE International Conference on Mechatronics and Automation, ICMA 2009 :1223-1227
Appears in Collections:	[生物資訊與醫學工程學系 ] 會議論文

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