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An efficient ensemble pruning approach based on simple coalitional games
DOI:10.1016/j.inffus.2016.06.003.png)
摘要
En 中文
We propose a novel ensemble pruning methodology using non-monotone Simple Coalitional Games, termed SCG-Pruning. Our main contribution is two-fold: (1) Evaluate the diversity contribution of a classifier based on Banzhaf power index. (2) Define the pruned ensemble as the minimal winning coalition made of the members that together exhibit moderate diversity. We also provide a new formulation of Banzhaf power index for the proposed game using weighted voting games. To demonstrate the validity and the effectiveness of the proposed methodology, we performed extensive statistical comparisons with several ensemble pruning techniques based on 58 UCI benchmark datasets. The results indicate that SCG-Pruning outperforms both the original ensemble and some major state-of-the-art selection approaches. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
Ensemble pruning
Simple coalitional game
Banzhaf power index
Weighted voting game
Diversity
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期刊
IF:
15.5
论文数:
4.2K
被引数:
2.7W
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引用论文
Ensemble of extreme learning machine for remote sensing image classification用于遥感图像分类的极限学习机集成
NEUROCOMPUTING
IF6.5

