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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/8647

    Title: Theory and application of the composed fuzzy measure of L-measure and delta-measures
    Authors: Liu, Hsiang-Chuan;Chen, Chin-Chun;Wu, Der-Bang;Sheu, Tian-Wei
    Contributors: Department of Bioinformatics
    Keywords: Linear regression;Mean square error;Choquet integral regression model;Composed fuzzy measure;Delta-measure;Gamma-support;Lambda-measure;P-measure
    Date: 2009-08
    Issue Date: 2010-04-07 21:21:16 (UTC+8)
    Publisher: Asia University
    Abstract: The well known fuzzy measures, λ-measure and P-measure, have only one formulaic solution. Two multivalent fuzzy measures with infinitely many solutions were proposed by our previous works, called L-measure and δ-measure, but the former do not include the additive measure as the latter and the latter has not so many measure solutions as the former. Due to the above drawbacks, in this paper, an improved fuzzy measure composed of above both, denoted L<inf>δ</inf> -measure, is proposed. 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 L<inf>δ</inf> -measure, L-measure, δ-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 extensional L-measure based on γ-support outperforms others forecasting models.
    Relation: WSEAS Transactions on Systems and Control 4(8):359-368
    Appears in Collections:[生物資訊與醫學工程學系 ] 期刊論文

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