Third International Conference on Natural Computation (ICNC 2007) (2007)
Haikou, Hainan, China
Aug. 24, 2007 to Aug. 27, 2007
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICNC.2007.185
Jiang Wu , Sichuan University, China; Southwestern University of Finance and Economics, China
Changjie Tang , Sichuan University, China
Jun Zhu , National Center for Birth Defects Monitoring, China
Taiyong Li , Sichuan University, China; Southwestern University of Finance and Economics, China
Lei Duan , Sichuan University, China
Chuan Li , Sichuan University, China
Li Dai , National Center for Birth Defects Monitoring, China
Ensemble of classifiers is a learning paradigm where a set of classifiers are jointly used to improve the classification accuracy. The main contributions of this paper include: (1) proposing a new concept named attribute selection set based on Gene Expression Programming (GEP), (2) analyzing the principle of classifier ensemble, (3) proposing an Attribute- Oriented Ensemble Classifier Based on Niche Gene Expression Programming (AO-ECNG) to improve the accuracy of sub-classifiers and at the same time maintain the diversity among them, and (4) analyzing the relationship between predictive accuracy and ensemble size. Experimental results on 10 datasets suggest that AO-ECNG increases the predictive accuracy by 2.51%, 1.66%, 1.33% and 1% respectively compared with single GEP-classifiers, Bagging, AdaBoost, and GEFS.
C. Li et al., "An Attribute-Oriented Ensemble Classifier Based on Niche Gene Expression Programming," Third International Conference on Natural Computation (ICNC 2007)(ICNC), Haikou, Hainan, China, 2007, pp. 525-529.