Choquet integral with respect to extensional L-measure and its application

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Title:	Choquet integral with respect to extensional L-measure and its application
Authors:	Liu, Hsiang-Chuan;Chen, Chin-Chun;Jheng, Yu-Du;Chien, Maw-Fa
Contributors:	Department of Bioinformatics
Keywords:	Fuzzy systems;Linear regression;Mean square error;Choquet integral;Closed form;Cross validation;Forecasting models;Fuzzy measures;L-measure;Multiple linear regression models;Regression model;Ridge regression
Date:	2009
Issue Date:	2010-04-08 12:05:55 (UTC+0)
Publisher:	Asia University
Abstract:	The well known fuzzy measures, γ-measure and P-measure, have only one formulaic solution. An multivalent fuzzy measure with infinitely many solutions of closed form based on P-measure was proposed by our previous work, called L-measure, In this paper, A further improved fuzzy measure, called extensional L-measure, is proposed. This new fuzzy measure is proved that it is not only an extension of L-measure but also can be considered as an extension of the γ-measure and P-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 extensional L-measure, L-measure, γ-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 extensional L-measure based on γ-support outperforms others forecasting models. © 2009 IEEE.
Relation:	6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009 6 :131-136
Appears in Collections:	[生物資訊與醫學工程學系 ] 會議論文

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