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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/8773


    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 20:05:57 (UTC+8)
    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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