Accession number:20090111824481
Title:A new classification algorithm combining choquet integral and logistic regression
Authors:Liu, Hsiang-Chan (1); Jheng, Yu-Du (2); Chen, Guey-Shya (2);
Jeng, Bai-Cheng (2)
Author affiliation:(1) Department of Bioinformatics, Asia University Taiwan, Taiwan; (2) Graduate Institute of Educational Measurement and Statistics, National Taichung University, Taiwan
Corresponding author:Liu, H.-C.
Source title:Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC
Abbreviated source title:Proc. Int. Conf. Mach. Learn. Cybern., ICMLC Volume:6
Monograph title:Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC
Issue date:2008
Publication year:2008 Pages:3072-3077
Article number:4620936 Language:English
ISBN-13:9781424420964
Document type:Conference article (CA)
Conference name:7th International Conference on Machine Learning and Cybernetics, ICMLC
Conference date:July 12, 2008 - July 15, 2008 Conference location:Kunming, China
Conference code:74802
Publisher:Inst. of Elec. and Elec. Eng. Computer Society, 445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States Abstract:Logistic regression algorithm and SVM algorithm are two well-known classification algorithms but when the multi-collinearity between independent variables occurs in above two algorithms, their classifying performance will always be not good. Due to this reason, we firstly proposed a pared-down MLE method in this study to improve the logistic regression algorithm for no needing to group the original data. Secondly, we proposed a novel classification
algorithm combining the Choquet integral with respect to the
λ-measure based on y-support proposed by our previous work and the improved logistic regression algorithm to further improve the above situation. For evaluating the performances of the SVM, logistic regression and our new algorithm with y-support based on X-measure and P-support respectively, a real data experiment by using Leave-one-out Cross-Validation accuracy is conducted.
Experimental result shows that the proposed classification algorithm combining Choquet integral regression model with y-support based on λ-measure has the best performance. ©2008 IEEE.
Number of references:12
Main heading:Regression analysis
Controlled terms:Control theory - Cybernetics - Integral equations - Learning systems - Logistics - Robot learning - Support vector machines
Uncontrolled terms:Choquet integral - Classification algorithms - Collinearity - Independent variables - Leave one outs - Logistic regression - Logistic regression algorithms - New algorithms - Real datums - Regression models - SVM - SVM algorithms
Classification code:922.2 Mathematical Statistics - 921.2 Calculus - 912 Industrial Engineering and Management - 731.5 Robotics - 731.1 Control Systems - 723.5 Computer Applications - 723.4 Artificial Intelligence - 723 Computer Software, Data Handling and Applications - 461.9 Biology - 461.4 Ergonomics and Human Factors Engineering - 404.1 Military Engineering
DOI:10.1109/ICMLC.2008.4620936 Database:Compendex
Compilation and indexing terms, Copyright 2009 Elsevier Inc.
