A Novel Fuzzy Measure and its Choquet Integral Regression Model

The well known fuzzy measures, λ -measure and P-measure, have only one formulaic solution, the former is not a closed form, and the later is not sensitive. In this study, Sugeno, and Choquet integral regression models with a novel fuzzy measure, L -measure, are proposed. The proposed L-measure has i...

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Bibliographic Details
Main Authors: Liu, Hsiang-Chuan, Jheng, Yu-Du, Lin, Wen-Chih, Chen, Guey-Shya
Format: Conference Proceeding
Language:English
Subjects:
Asia
Bioinformatics
Biotechnology
Choquet integral
Choquet integral regression model
Cybernetics
Fuzzy sets
Integral equations
L -measure
Linear regression
Machine learning
P-measure
Performance evaluation
Predictive models
λ -measure
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Summary:The well known fuzzy measures, λ -measure and P-measure, have only one formulaic solution, the former is not a closed form, and the later is not sensitive. In this study, Sugeno, and Choquet integral regression models with a novel fuzzy measure, L -measure, are proposed. The proposed L-measure has infinitely many closed-form solutions. For evaluating the proposed regression models with different fuzzy measures, a real data experiment by using a 5-fold cross-validation mean square error (MSE) is conducted. The performances of Sugeno, and Choquet integral regression models with fuzzy measure based on λ -measure, P-measure, and L -measure respectively, a ridge regression model, and a multiple linear regression model are compared. Experimental result shows that the Choquet integral regression models with L -measure outperforms others forecasting models.
ISSN:2160-133X
DOI:10.1109/ICMLC.2007.4370362