@article{(Open Science Index):https://publications.waset.org/pdf/6392,
	  title     = {Combining Bagging and Additive Regression },
	  author    = {Sotiris B. Kotsiantis},
	  country	= {},
	  institution	= {},
	  abstract     = {Bagging and boosting are among the most popular re-sampling ensemble methods that generate and combine a diversity of regression models using the same learning algorithm as base-learner. Boosting algorithms are considered stronger than bagging on noise-free data. However, there are strong empirical indications that bagging is much more robust than boosting in noisy settings. For this reason, in this work we built an ensemble using an averaging methodology of bagging and boosting ensembles with 10 sub-learners in each one. We performed a comparison with simple bagging and boosting ensembles with 25 sub-learners on standard benchmark datasets and the proposed ensemble gave better accuracy.
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {1},
	  number    = {1},
	  year      = {2007},
	  pages     = {130 - 136},
	  ee        = {https://publications.waset.org/pdf/6392},
	  url   	= {https://publications.waset.org/vol/1},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 1, 2007},
	}