ABSTRACT The random subspace method (RSM) has proved its excellence in numbers of pattern recognition tasks. However, the standard RSM is limited owing to the randomness in its feature selection procedure that is likely to lead to feature subset having poor class separability. In this paper, a proposal for a margin-based criterion has been presented for the evaluation of the true significance of the features, together with the true classification ability of base classifiers, so that both the training phase and integration phase of standard RSM could be enhanced. In the training phase, the random feature selection procedure is enhanced using a weighted random feature selection procedure, in order to improve the classification ability of the base classifier. In the integration phase, the simple majority voting strategy is enhanced using a weighted majority voting strategy for the purpose of assigning the base classifiers with poor classification ability to the lower voting weights. Experimental results on 30 benchmark datasets, together with 6 high-dimensional datasets prove that the recommended approach is capable of better providing classification ability to the usual classification task, in addition to the high-dimensional classification task.

中文翻译：

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用边际理论丰富随机子空间方法——高维分类任务的解决方案

摘要 随机子空间方法 (RSM) 已证明其在模式识别任务数量上的卓越性。然而，标准 RSM 由于其特征选择过程中的随机性而受到限制，这可能导致特征子集具有较差的类可分离性。在本文中，提出了一种基于边际标准的建议，用于评估特征的真实重要性以及基分类器的真实分类能力，以便标准 RSM 的训练阶段和集成阶段都可以增强。在训练阶段，使用加权随机特征选择过程增强随机特征选择过程，以提高基分类器的分类能力。在整合阶段，使用加权多数投票策略增强了简单多数投票策略，目的是将分类能力较差的基分类器分配给较低的投票权重。在 30 个基准数据集和 6 个高维数据集上的实验结果证明，除了高维分类任务外，推荐的方法能够更好地为通常的分类任务提供分类能力。