论文标题
随机的输音箱
Random Hyperboxes
论文作者
论文摘要
本文提出了一个简单而功能强大的集合分类器，称为随机输音箱，该分类器由基于单独的HAPEBOX的分类器构建，该分类器在样本的随机子集和训练集的特征空间上训练。我们还根据基于单个HAPEBOX的分类器的强度以及它们之间的相关性显示了提出的分类器的概括误差。使用精心选择的说明性示例分析了所提出的分类器的有效性，并使用统计测试方法通过20个数据集进行了经验与其他流行的单个和集合分类器进行比较。实验结果证实，我们所提出的方法优于其他模糊的最小神经网络，流行的学习算法，并且与其他集合方法具有竞争力。最后，我们确定了与真实数据集的概括误差范围有关的现有问题，并告知潜在的研究方向。
This paper proposes a simple yet powerful ensemble classifier, called Random Hyperboxes, constructed from individual hyperbox-based classifiers trained on the random subsets of sample and feature spaces of the training set. We also show a generalization error bound of the proposed classifier based on the strength of the individual hyperbox-based classifiers as well as the correlation among them. The effectiveness of the proposed classifier is analyzed using a carefully selected illustrative example and compared empirically with other popular single and ensemble classifiers via 20 datasets using statistical testing methods. The experimental results confirmed that our proposed method outperformed other fuzzy min-max neural networks, popular learning algorithms, and is competitive with other ensemble methods. Finally, we identify the existing issues related to the generalization error bounds of the real datasets and inform the potential research directions.
