A CHOQUET INTEGRAL REGRESSION MODEL BASED ON A NEW FUZZY MEASURE

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.

Download Full-text

An Evaluation Model Based on Rough Sets

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.602-605.3379 ◽

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.

Download Full-text

A Novel Fuzzy Measure and its Choquet Integral Regression Model

2007 International Conference on Machine Learning and Cybernetics ◽

10.1109/icmlc.2007.4370362 ◽

2007 ◽

Cited By ~ 10

Author(s):

Hsiang-Chuan Liu ◽

Yu-Du Jheng ◽

Wen-Chih Lin ◽

Guey-Shya Chen

Keyword(s):

Regression Model ◽

Choquet Integral ◽

Fuzzy Measure

Download Full-text

Choquet Integral with Respect to High Order Extensional L- Measure

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.44-47.3579 ◽

2010 ◽

Vol 44-47 ◽

pp. 3579-3583 ◽

Cited By ~ 3

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.

Download Full-text