DocumentCode :
2247446
Title :
A Monte Carlo simulation study on Choquet integral with respect to different fuzzy measures
Author :
Yao, Hsu-chan ; Liu, Hsiang-chuan ; Jheng, Yu-Du ; Chang, Chun-jey
Author_Institution :
Grad. Inst. of Educ. Meas. & Stat., Taichung Univ., Taichung, Taiwan
Volume :
5
fYear :
2010
fDate :
11-14 July 2010
Firstpage :
2366
Lastpage :
2371
Abstract :
In this paper, a hybrid method based on Monte Carlo simulation study method and 5-fold cross-validation MSE method is used, a simulation experiment is conducted for comparing the performances of a multiple linear regression model, a ridge regression model, and the Choquet integral regression model with respect to three well known fuzzy measures, P-measure, λ-measure and L-measure, respectively. The result shows that the Choquet integral regression model with respect to L-measure outperforms other forecasting models.
Keywords :
Monte Carlo methods; fuzzy set theory; mean square error methods; λ-measure; 5-fold cross-validation MSE method; Choquet integral regression model; L-measure; Monte Carlo simulation study method; P-measure; fuzzy measures; multiple linear regression model; ridge regression model; Correlation; Data models; Linear regression; Machine learning; Mathematical model; Monte Carlo methods; Predictive models; Choquet integral; Fuzzy measure; L-measure; Simulation;
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.2010.5580667
Filename :
5580667
Link To Document :
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