A Novel Fuzzy Measure and its Choquet Integral Regression Model

2007 ◽  
Author(s):  
Hsiang-Chuan Liu ◽  
Yu-Du Jheng ◽  
Wen-Chih Lin ◽  
Guey-Shya Chen
Keyword(s):  
Regression Model ◽  
Choquet Integral ◽  
Fuzzy Measure

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.

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 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

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.

CHOQUET INTEGRAL MODELS AND INDEPENDENCE CONCEPTS IN MULTIATTRIBUTE UTILITY THEORY

2000 ◽  
Vol 08 (04) ◽  
pp. 385-415 ◽  
Author(s):  
T. MUROFUSHI ◽  
M. SUGENO
Keyword(s):  
Utility Function ◽  
Utility Theory ◽  
Choquet Integral ◽  
Fuzzy Measure ◽  
Multiattribute Utility ◽  
Preference Relations ◽  
Multiattribute Utility Theory ◽  
A Value ◽  
Utility Independence

This paper discusses multiattribute preference relations compatible with a value/utility function represented by the Choquet integral with respect to a fuzzy measure, and shows that the additivity of the fuzzy measure is equivalent to each of mutual preferential independence, mutual weak difference independence, mutual difference independence, mutual utility independence, and additive independence.

An Evaluation Model Based on Rough Sets

2014 ◽  
Vol 602-605 ◽  
pp. 3379-3383
Author(s):  
Yong Sheng Liu ◽  
Zan Zhang
Keyword(s):  
Decision Making ◽  
Information System ◽  
Knowledge Discovery ◽  
Rough Sets ◽  
Choquet Integral ◽  
Evaluation Model ◽  
Fuzzy Measure ◽  
Model Based

In multiattribute decision making, it is critical to indentify the importance degree of attributes before the overall assessment of the alternatives. In this paper, we give a measurement of importance degree of attributes based on knowledge discovery in the decision information system, which satisfies the conditions of fuzzy measure. Further, we construct an evaluation model combined Choquet integral with the importance degree measure. The case study illustrates the validity and the effectiveness of the method.

Aggregation of neural classifiers using Choquet integral with respect to a fuzzy measure

2018 ◽  
Vol 292 ◽  
pp. 151-164 ◽  
Author(s):  
Andre G.C. Pacheco ◽  
Renato A. Krohling
Keyword(s):  
Choquet Integral ◽  
Fuzzy Measure

Fuzzy Three-valued Switching Functions Using Choquet Integral

2003 ◽  
Vol 7 (1) ◽  
pp. 47-52 ◽  
Author(s):  
Eiichiro Takahagi ◽  
Keyword(s):  
Choquet Integral ◽  
Fuzzy Measure ◽  
Unknown Inputs ◽  
Switching Functions ◽  
True False ◽  
Inputs And Outputs

Fuzzy three-valued switching functions have true, false and unknown inputs and outputs values. The functions are calculated by Choquet integral whose fuzzy measure values are assigned from normal three-valued switching functions. The functions are extension of normal three-valued functions and satisfy the monotonicity with respect to vagueness.

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