A spectral clustering based ensemble pruning approach
A spectral clustering based ensemble pruning approach
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DOI：
10.1016/j.neucom.2014.02.030
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发表时间：
2014-09
期刊：
影响因子：
6
通讯作者：
Huaxiang Zhang;Linlin Cao
中科院分区：
文献类型：
--
作者：
Huaxiang Zhang;Linlin Cao
提出了一种新的Bagging集成分类器剪枝方法。大多数研究的剪枝方法采用启发式函数对集成中的分类器进行排序，并从排序后的集成中选择部分分类器，因此所选择的分类器中可能存在冗余。基于选择的分类器应该准确和多样的思想，我们定义分类器的相似性，根据预测精度和多样性，并介绍了一种基于谱聚类的分类器选择方法（SC）。SC基于分类器相似性将分类器分组为两个聚类，并在集成中保留一个分类器聚类。实验结果表明，SC是有竞争力的分类精度。
This paper introduces a novel bagging ensemble classifier pruning approach. Most investigated pruning approaches employ heuristic functions to rank classifiers in the ensemble, and select part of them from the ranked ensemble, so redundancy may exist in the selected classifiers. Based on the idea that the selected classifiers should be accurate and diverse, we define classifier similarity according to the predictive accuracy and the diversity, and introduce a Spectral Clustering based classifier selection approach (SC). SC groups the classifiers into two clusters based on the classifier similarity, and retains one cluster of classifiers in the ensemble. Experimental results show that SC is competitive in terms of classification accuracy.