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首页> 外文会议>Proceedings of the 2007 International Conference on Machine Learning and Cybernetics >SAMPLING BASED ON MINIMAL CONSISTENT SUBSET FOR HYPER SURFACE CLASSIFICATION
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SAMPLING BASED ON MINIMAL CONSISTENT SUBSET FOR HYPER SURFACE CLASSIFICATION

机译：基于最小一致性子集的超表面分类采样

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摘要

For Hyper Surface Classification (HSC), based on the concept of Minimal Consistent Subset for a disjoint Cover set (MCSC), a judgmental sampling method is proposed to select a representative subset from the original sample set in this paper.The sampling method depends on sample distribution.HSC can directly solve the nonlinear multi-class classification problems and observe the sample distribution.The sample distribution is obtained by adaptively dividing the sample space, and the classification model of hyper surface is directly used to classify large database based on Jordan Curve Theorem in Topology while sampling for MCSC.The number of MCSC is calculated.MCSC has the same classification model with the entire sample set and can totally reflect its classification ability.For any subset of the sample set that contains MCSC, the classification ability remains the same.Moreover, a formula is put forward that can predict the testing accuracy exactly when some samples are deleted from MCSC.So MCSC is the best way of sampling from the original sample set for Hyper Surface Classification method.
机译：对于超曲面分类（HSC），基于不连续覆盖集（MCSC）的最小一致子集的概念，本文提出了一种判断性抽样方法，以从原始样本集中选择一个代表性子集。 HSC可以直接解决非线性多类分类问题并观察样本分布，通过自适应划分样本空间获得样本分布，直接使用超曲面分类模型对基于约旦曲线的大型数据库进行分类MCSC采样时的拓扑定理。计算MCSC的数量.MCSC与整个样本集具有相同的分类模型，可以完全反映其分类能力。对于包含MCSC的样本集的任何子集，分类能力仍为此外，提出了一个公式，可以准确预测从MCS中删除一些样本时的测试准确性。 C.因此，MCSC是从“超曲面分类”方法的原始样本集中进行采样的最佳方法。

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