减少离群值对单类分类决策规则的影响

Proceedings of the 4th International Conference on Future Networks and Distributed Systems Pub Date : 2020-11-26 DOI:10.1145/3440749.3442662

A. Larin, O. Seredin, A. Kopylov

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引用次数: 0

摘要

D. Tax基于支持向量数据描述(SVDD)提出了一种改进的一类分类准则，减少了异常值对一类分类决策规则的影响。该优化方法利用不可微目标函数替换为光滑目标函数。通过与现有一类方法的对比实验研究，证明了该准则在异常检测中的优越性。

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Reducing the Impact of Outliers on the One-Class Classification Decision Rule

A modified version of one-class classification criterion reducing the impact of outliers on the one-class classification decision rule is proposed based on support vector data description (SVDD) by D. Tax. The optimization method utilizes the substitution of nondifferentiable objective function by the smooth one. A comparative experimental study of existing one-class methods shows the superiority of the proposed criterion in anomaly detection.

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Proceedings of the 4th International Conference on Future Networks and Distributed Systems

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