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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/15286


    Title: Choquet integral algorithm for thermostable proteins based on Hurst exponent and generalized L-measure.
    Authors: 劉湘川;Liu, Hsiang-Chuan
    Contributors: 生物與醫學資訊學系
    Keywords: Hurst exponent;Choquet integral;L-measure;Maximized;L-measure;Generalized L-measure
    Date: 2010
    Issue Date: 2012-11-23 11:33:27 (UTC+8)
    Abstract: "Due to the lengths of amino symbolic sequences of protein
    are always different, any regression model can not be used for
    predicting the temperature of thermostable proteins without
    adequate pretreatment. We need to transfer each amino
    symbolic sequence as some useful physicochemical quantities by
    using Hurst exponent first, and then, some regression models
    may be considered. Combining the Hurst exponent and the
    Choquet integral regression model with respect to the well
    known fuzzy measure, L-measure, is first proposed in last year.
    Although L-measure is a multivalent measure and better than
    the well known fuzzy measures, -measure and P-measure,
    however it does not contain the additive measure and does not
    attain the largest fuzzy measure, B-measure. In accordance with
    above drawbacks, an improved L-measure, called generalized
    L-measure, was proposed, but this new fuzzy measure has not
    been used for combining the Hurst exponent to predict the
    temperature of thermostable proteins yet. In this paper, the
    sensitive comparison property between two completed fuzzy
    measures and some more properties of generalized L-measure
    are discussed, the method combining the Hurst exponent and
    the Choquet integral regression model with respect to
    generalized L-measure is proposed, a 5-fold Cross-Validation
    MSE is conducted. Experimental result shows that the Choquet
    integral regression model based on Hurst exponent and
    generalized L-measure has the best performance, it is better
    than Choquet integral regression model based on Hurst
    exponent and other fuzzy measures, including completed
    L-measure, L-measure, Lambda-measure, and P-measure, and
    the traditional prediction models, ridge regression and multiple
    linear regression models"
    Relation: Proceedings of the ninth International Conference on Machine Learning and Cybernetics.
    Appears in Collections:[生物資訊與醫學工程學系 ] 會議論文

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