Clustering-Based Ensembles as an Alternative to Stacking
Clustering-Based Ensembles as an Alternative to Stacking
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DOI：
10.1109/tkde.2013.49
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发表时间：
2014-09
影响因子：
8.9
通讯作者：
A. Jurek;Y. Bi;Shengli Wu;C. Nugent
中科院分区：
文献类型：
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作者：
A. Jurek;Y. Bi;Shengli Wu;C. Nugent
生成分类器集成的最流行的技术之一是基于元学习方法的堆叠。在本文中，我们介绍了一种替代方法，堆叠是基于聚类分析。与堆叠类似，来自验证集的实例最初由所有基本分类器分类。每个分类器的输出随后被认为是实例的新属性。在此之后，根据实例的新属性和原始属性的一个小子集将验证集划分为聚类。对于每个聚类，我们找到它的质心并计算它的类标签。质心的集合被认为是元分类器。实验结果表明，在22个数据集中的12个数据集上，新方法的性能优于所有基准方法，即多数投票、Stacking J48、Stacking LR、AdaBoost J48和随机森林。所提出的方法有两个优点：它是非常强大的相对较小的训练集，它可以应用于半监督学习问题。我们提供了一个理论研究所提出的方法。这表明，为了使该方法成功，集成中应用的基本分类器应该具有大于50%的准确度。
One of the most popular techniques of generating classifier ensembles is known as stacking which is based on a meta-learning approach. In this paper, we introduce an alternative method to stacking which is based on cluster analysis. Similar to stacking, instances from a validation set are initially classified by all base classifiers. The output of each classifier is subsequently considered as a new attribute of the instance. Following this, a validation set is divided into clusters according to the new attributes and a small subset of the original attributes of the instances. For each cluster, we find its centroid and calculate its class label. The collection of centroids is considered as a meta-classifier. Experimental results show that the new method outperformed all benchmark methods, namely Majority Voting, Stacking J48, Stacking LR, AdaBoost J48, and Random Forest, in 12 out of 22 data sets. The proposed method has two advantageous properties: it is very robust to relatively small training sets and it can be applied in semi-supervised learning problems. We provide a theoretical investigation regarding the proposed method. This demonstrates that for the method to be successful, the base classifiers applied in the ensemble should have greater than 50% accuracy levels.