Choquet integral with respect to the generalized L-measure and its application

Liu, Hsiang-Chuan; Wu, Der-Bang; Chen, Wei-Sung; Jheng, Yu-Du

doi:10.1109/3ca.2010.5533876

Cited by 2 publications

(2 citation statements)

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“…An multivalent fuzzy measure with infinitely many solutions of fuzzy measures, called L-measure, is proposed [9], but it does not attain to the largest fuzzy measure, B-measure [10], for overcome the above drawback, an improved fuzzy measure, completed L-measure, denoted C L -measure, is proposed [15]. Recently, it is found that both C L -measure and L-measure do not include the additive measure and Ȝ-measure, therefore, a further improved fuzzy measure, called generalize L-measure, denoted G L -measure, is proposed by Liu [16]. In which, the proof of the properties of this new measure are omitted for lack of space, and up to now, this new measure has not been used for combining the Hurst exponent to predict the temperature of thermostable proteins yet.…”

Section: Introductionmentioning

confidence: 99%

“…When there are interactions among independent variables including the amino symbolic sequence of proteins, traditional multiple linear regression models and ridge regression models do not perform well enough [8], the Choquet integral regression models based on some fuzzy measures were used to improve this situation [8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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Choquet integral algorithm for thermostable proteins based on Hurst exponent and generalized L-measure

Chang

Liu

et al. 2010

2010 International Conference on Machine Learning and Cybernetics

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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, Ȝ-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.

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Section: Introductionmentioning

confidence: 99%