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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/8789


    Title: The choquet integral with respect to R-measure based on γ-support
    Authors: Liu, Hsiang-Chuan;Tu, Yu-Chieh;Huang, Wen-Chun;Chen, Chin-Chun
    Contributors: Department of Bioinformatics
    Keywords: Fuzzy logic;Fuzzy systems;Integral equations;Mathematical models;Mean square error;Choquet integrals;Cross validations;Fuzzy measures;Improved methods;Improved models;Independent variables;Mean squares;Multicollinearity;Multiple regression models;Real datums;Regression models;Ridge regressions
    Date: 2008
    Issue Date: 2010-04-08 20:06:07 (UTC+8)
    Publisher: Asia University
    Abstract: When the multicollinearity within independent variables occurs in the multiple regression models, its performance will always be poor. Replacing the above models with the ridge regression model is the traditional improved method. In our previous work, we found that, the Choquet integral regression model with λ-measure based on the new support, γ-support, proposed by us has the best performance than before. In this study, for finding the further improved model, we replaced two well known fuzzy measures, P-measure and λ-measure with our new fuzzy measure, R-measure in Choquet integral regression model with the new support, γ-support. For comparing the Choquet integral regression model with P-measure, λ-measure and R-measure based on two different fuzzy supports, V-support and γ-support, respectively, the traditional multiple regression model and the ridge regression model, 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 R-measure based on γ-support has the best performance. © 2008 IEEE.
    Relation: Proceedings - 5th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2008 :645-649
    Appears in Collections:[生物資訊與醫學工程學系 ] 會議論文

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