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An Integrated Algorithm of Liu's Generalized Lambda-Measure Based Choquet Integral and Hurst Exponent

机译:Liu广义Lambda测度的Choquet积分和Hurst指数的集成算法

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摘要

In this paper, a novel integrated algorithm of Choquet integral with respect to Liu''s generalized Lambda-measure and Hurst exponent was proposed, it can be used for predicting the temperature of thermos table proteins on some useful physicochemical quantities of each amino symbolic sequence with different lengths. A real data set of the temperature of thermos table proteins with 5-fold cross-validation MSE is conducted, for comparing the performances of the integrated algorithm of Hurst exponent and Choquet integral regression model with respect to six measures, P-measure, λ-measure, L-measure, extensional L-measure, generalized L-measure and Liu, s generalized Lambda-measure, respectively, and two traditional regression model, multiple regression model and ridge regression model, the results show that the integrated algorithm of Hurst exponent and Choquet integral regression model with respect to Liu''s generalized Lambda-measure has the best performance.
机译:本文针对Liu的广义Lambda测度和Hurst指数,提出了一种新颖的Choquet积分综合算法,可用于预测每个氨基符号序列在一些有用的理化量上的保温杯蛋白温度。不同的长度。进行了5倍交叉验证MSE的热水瓶表蛋白温度的真实数据集,用于比较Hurst指数和Choquet积分回归模型的集成算法相对于6个测量值(P测量值,λ-测量值)的性能测度,L测度,扩展L测度,广义L测度和Liu广义Lambda测度,以及两个传统回归模型(多元回归模型和岭回归模型),结果表明,Hurst指数的集成算法和针对刘氏广义Lambda测度的Choquet积分回归模型具有最佳性能。

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