DocumentCode :
2247847
Title :
Choquet integral algorithm for thermostable proteins based on Hurst exponent and generalized L-measure
Author :
Chang, Horng-Jinh ; Chang, Pei-chun ; Liu, Hsiang-chuan ; Lee, Kuei-jen ; Wang, Jing-doo ; Liu, Yu-Lung
Author_Institution :
Dept. of Bus. Adm., Asia Univ., Wufeng, Taiwan
Volume :
5
fYear :
2010
fDate :
11-14 July 2010
Firstpage :
2383
Lastpage :
2389
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, λ-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.
Keywords :
biocomputing; fuzzy set theory; macromolecules; proteins; regression analysis; λ-measure; 5-fold cross-validation MSE; Choquet integral algorithm; Choquet integral regression model; Hurst exponent; Lambda-measure; P-measure; amino symbolic sequences; fuzzy measure; generalized L-measure; multiple linear regression models; physicochemical quantity; regression model; temperature prediction; thermostable proteins; Additives; Amino acids; Density functional theory; Density measurement; Predictive models; Proteins; Temperature measurement; Choquet integral; Generalized L-measure; Hurst exponent; L-measure; Maximized L-measure;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-6526-2
Type :
conf
DOI :
10.1109/ICMLC.2010.5580685
Filename :
5580685
Link To Document :
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