한국통계학회:학술대회논문집 (Proceedings of the Korean Statistical Society Conference)
- 한국통계학회 2005년도 추계 학술발표회 논문집
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- Pages.141-146
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- 2005
Improving Bagging Predictors
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Kim, Hyun-Joong
(Department of Applied Statistics, Yonsei University) ;
- Chung, Dong-Jun (Department of Applied Statistics, Yonsei University)
- 발행 : 2005.11.04
초록
Ensemble method has been known as one of the most powerful classification tools that can improve prediction accuracy. Ensemble method also has been understood as ‘perturb and combine’ strategy. Many studies have tried to develop ensemble methods by improving perturbation. In this paper, we propose two new ensemble methods that improve combining, based on the idea of pattern matching. In the experiment with simulation data and with real dataset, the proposed ensemble methods peformed better than bagging. The proposed ensemble methods give the most accurate prediction when the pruned tree was used as the base learner.