A Novel Prediction Algorithm of Thermostable Proteins by Using Hurst Exponent and Choquet Integral Regression Model

2008 ◽  
Author(s):  
Hsiang-Chuan Liu ◽  
Yu-Chieh Tu ◽  
Bai-Cheng Jeng ◽  
Tung-Sheng Liu
Keyword(s):  
Regression Model ◽  
Hurst Exponent ◽  
Choquet Integral ◽  
Prediction Algorithm

Forecasting stock market price by using fuzzified Choquet integral based fuzzy measures with genetic algorithm for parameter optimization

2020 ◽  
Vol 54 (2) ◽  
pp. 597-614
Author(s):  
Shanoli Samui Pal ◽  
Samarjit Kar
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Linear Regression ◽  
Regression Model ◽  
Choquet Integral ◽  
Time Series Forecasting ◽  
Fuzzy Measure ◽  
Model Parameters ◽  
Forecasting Models ◽  
Non Linear

In this paper, fuzzified Choquet integral and fuzzy-valued integrand with respect to separate measures like fuzzy measure, signed fuzzy measure and intuitionistic fuzzy measure are used to develop regression model for forecasting. Fuzzified Choquet integral is used to build a regression model for forecasting time series with multiple attributes as predictor attributes. Linear regression based forecasting models are suffering from low accuracy and unable to approximate the non-linearity in time series. Whereas Choquet integral can be used as a general non-linear regression model with respect to non classical measures. In the Choquet integral based regression model parameters are optimized by using a real coded genetic algorithm (GA). In these forecasting models, fuzzified integrands denote the participation of an individual attribute or a group of attributes to predict the current situation. Here, more generalized Choquet integral, i.e., fuzzified Choquet integral is used in case of non-linear time series forecasting models. Three different real stock exchange data are used to predict the time series forecasting model. It is observed that the accuracy of prediction models highly depends on the non-linearity of the time series.

Importance-Performance Analysis Based on Choquet Integral with Respect to L-Measure

2010 ◽  
Vol 44-47 ◽  
pp. 3844-3848
Author(s):  
Shu Juan Lee ◽  
Hsiang Chuan Liu ◽  
Shih Ming Chen ◽  
Yu Du Jheng
Keyword(s):  
Performance Analysis ◽  
Regression Model ◽  
Choquet Integral ◽  
Ministry Of Education ◽  
Software Application ◽  
Simple Logistic Regression Model ◽  
Survey System ◽  
And Performance ◽  
Set Up ◽  
Simple Logistic

In this study, the Web-based Multi-Survey System was adopted, one or two choices available among five options, to set up questionnaire to get a nine-point scale with network programs. As an empirical research, which was conformed to the policy of Ministry of Education to promote technology abilities for teachers in Taichung County in 2009, the important and satisfied survey from Teachers’ Free Software Application workshops was analyzed by four approaches which were Importance-Performance Analysis, Simple Logistic regression model, Choquet integral regression model with respect to λ-measure, and Choquet integral regression model with respect to L-measure. By comparing MSE, Choquet integral regression model with L-measure obtained the best performance. Two crosshairs which were Hollenhorst’s overall mean and Choquet integral regression model with L-measure were positioned for I-P matrix. From results, the L-measure model had shown better sensitivity about quadrant distribution, and reflected participants’ real responses. At the same time, it was definitely known what to keep up the good work, concentrate here, or possible overkill through assessing Importance and Performance of teacher’s workshops.

A CHOQUET INTEGRAL REGRESSION MODEL BASED ON A NEW FUZZY MEASURE

2007 ◽  
pp. 1349-1355 ◽  
Author(s):  
HSIANG-CHUAN LIU ◽  
WEN-CHIH LIN ◽  
WEI-SHENG WENG
Keyword(s):  
Regression Model ◽  
Choquet Integral ◽  
Fuzzy Measure ◽  
Model Based

Choquet Integral with Respect to High Order Extensional L- Measure

2010 ◽  
Vol 44-47 ◽  
pp. 3579-3583 ◽  
Author(s):  
Hsiang Chuan Liu ◽  
Wei Sung Chen ◽  
Chin Chun Chen ◽  
Yu Du Jheng ◽  
Der Bang Wu
Keyword(s):  
Regression Model ◽  
Choquet Integral ◽  
Real Data ◽  
Multiple Regression Model ◽  
High Order ◽  
Data Set ◽  
Model Based ◽  
Result Show ◽  
Two Measures ◽  
Fold Cross Validation

In this paper, a generalized multivalent fuzzy measure of extensional L-measure, called high order extensional L-measure, is proposed. It is proved that if the value of order index is equal to one, this new measure is just the extensional L-measure, and the larger the value of order index is, the more sensitive it is. A real data set with 5- fold cross-validation MSE is conducted, for comparing the performances of the Choquet integral regression model based on this new measure with other four measures, P-measure and λ-measure, and authors’ two measures, L-measure and extensional L-measure, and two traditional regression model, multiple regression model and ridge regression model, the result show that the Choquet integral regression model based on this new measure has the best performance.

Choquet integral regression model based on Liu's second order multivalent fuzzy measure

2016 ◽  
pp. 825-829
Author(s):  
Hsiang-Chuan Liu ◽  
Hsien-Chang Tsai ◽  
Yen-Kuei Yu ◽  
Yi-Ting Mai
Keyword(s):  
Regression Model ◽  
Choquet Integral ◽  
Second Order ◽  
Fuzzy Measure ◽  
Model Based
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