A comparison on choquet integral with respect to different information-based fuzzy measures

Accession number:20094612446809

Title: A comparison on choquet integral with respect to different information-based fuzzy measures

Authors: Chang, Horng-Jinh (1); Liu, Hsiang-Chuan (2); Tseng, Shang- Wen (3); Chang, Fengming M. (4)

Author affiliation:(1) Department of Business Administration, Asia University, Wufeng, Taiwan; (2) Department of Bioinformatics, Asia University, Wufeng, Taiwan; (3) Department of Computer Science and Information Engineering, Asia University, Wufeng, Taiwan; (4) Department of Information Science and Applications

Corresponding author:Chang, H.-J.

([email protected])

Source title: Proceedings of the 2009 International Conference on Machine Learning and Cybernetics

Abbreviated source title:Proc. Int. Conf. Mach. Learn. Cybern.

Volume:6

Monograph title:Proceedings of the 2009 International Conference on Machine Learning and Cybernetics

Issue date:2009

Publication year:2009 Pages:3161-3166

Article number:5212801 Language:English

ISBN-13:9781424437030

Document type:Conference article (CA)

Conference name:2009 International Conference on Machine Learning and Cybernetics

Conference date:July 12, 2009 - July 15, 2009 Conference location:Baoding, China

Conference code:78063

Publisher:IEEE Computer Society, 445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States

Abstract:In this paper, for grouped data, three kinds of the Choquet integral regression models with fuzzy measures based on joint entropy, complexity and multiple mutual information is considered.

The above three fuzzy measures are called, E-measure, C-measure and M-measure, respectively. For evaluating the Choquet integral

regression models with these three information-based fuzzy

measures, a real grouped data experiment by using a 5-fold cross validation accuracy is conducted. The performances of the Choquet integral regression models based on these three fuzzy measures, respectively, and the traditional multiple linear regression model are compared. Experimental result shows that the Choquet integral regression model based on our proposed M-measure has the best performance and it outperforms the Choquet integral regression model based on our previous proposed C-measure. © 2009 IEEE.

Number of references:9

Main heading:Integral equations

Controlled terms: Control theory - Cybernetics - Linear regression - Robot learning

Uncontrolled terms: C-measure - Choquet integral - Choquet integral regression model - E-measure - M-measure

Classification code:461.9 Biology - 723.4 Artificial Intelligence - 731.1 Control Systems - 731.5 Robotics - 921.2 Calculus - 922.2 Mathematical Statistics

DOI:10.1109/ICMLC.2009.5212801 Database:Compendex

Compilation and indexing terms, Copyright 2009 Elsevier Inc.