Minimal Consistent Subset for Hyper Surface Classification Method
Minimal Consistent Subset for Hyper Surface Classification Method
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DOI：
10.1142/s0218001408006132
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发表时间：
2008-02
期刊：
影响因子：
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通讯作者：
Qing He;Xiu-Rong Zhao;Zhongzhi Shi
中科院分区：
文献类型：
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作者：
Qing He;Xiu-Rong Zhao;Zhongzhi Shi
超曲面分类（HSC）是基于拓扑学中的Jordan曲线定理的一种简单有效的分类方法。为了从原始样本集中选出一个具有代表性的子集，本文研究了HSC的最小一致子集（MCS）。对于HSC方法，MCS的一个最重要的特点是它具有与整个样本数据集相同的分类模型，并能全面反映其分类能力。从这个角度来看，MCS是HSC从原始数据集采样的最佳方式。此外，由于MCS的最小值特性，每次删除或多次删除都会导致其泛化能力的降低，本文提出的公式可以准确地预测这种降低。
Hyper Surface Classification (HSC), which is based on Jordan Curve Theorem in Topology, has proven to be a simple and effective method for classifying a larger database in our previous work. To select a representative subset from the original sample set, the Minimal Consistent Subset (MCS) of HSC is studied in this paper. For HSC method, one of the most important features of MCS is that it has the same classification model as the entire sample dataset, and can totally reflect its classification ability. From this point of view, MCS is the best way of sampling from the original dataset for HSC. Furthermore, because of the minimum property of MCS, every single deletion or multiple deletions from it will lead to a reduction in generalization ability, which can be exactly predicted by the proposed formula in this paper.