ASIA unversity:Item 310904400/8647
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    题名: Theory and application of the composed fuzzy measure of L-measure and delta-measures
    作者: Liu, Hsiang-Chuan;Chen, Chin-Chun;Wu, Der-Bang;Sheu, Tian-Wei
    贡献者: Department of Bioinformatics
    关键词: Linear regression;Mean square error;Choquet integral regression model;Composed fuzzy measure;Delta-measure;Gamma-support;Lambda-measure;P-measure
    日期: 2009-08
    上传时间: 2010-04-07 21:21:16 (UTC+8)
    出版者: Asia University
    摘要: 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.
    關聯: WSEAS Transactions on Systems and Control 4(8):359-368
    显示于类别:[生物資訊與醫學工程學系 ] 期刊論文

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