用负样本改进核函数，提升小规模单类分类效果
Generalized Reference Kernel With Negative Samples For Support Vector One-class Classification
- 基于负样本优化参考向量的核函数设计
- 在负样本极少时显著优于传统单类SVM和二分类SVM
- 无需标签参与训练，兼容原有SVM实现
本文针对存在少量负样本的小规模单类分类问题，提出广义参考核与负样本方法（GRKneg），用于支持向量机单类分类（OC-SVM）。研究了不同参考向量选取/生成方式，推荐适用于该场景的方法。所提方法在模型优化中不使用任何标签，仅通过负样本改进核函数，保持原生OC-SVM实现。在不同负样本数量下与标准OC-SVM及二分类支持向量机（SVM）对比，结果表明：使用径向基函数核时，本方法始终优于标准OC-SVM；当负样本充足时，二分类SVM表现更优；但在负样本最少的情况下，本方法明显优于二分类SVM。
原文摘要 · Abstract (English)
This paper focuses on small-scale one-class classification with some negative samples available. We propose Generalized Reference Kernel with Negative Samples (GRKneg) for One-class Support Vector Machine (OC-SVM). We study different ways to select/generate the reference vectors and recommend an approach for the problem at hand. It is worth noting that the proposed method does not use any labels in the model optimization but uses the original OC-SVM implementation. Only the kernel used in the process is improved using the negative data. We compare our method with the standard OC-SVM and with the binary Support Vector Machine (SVM) using different amounts of negative samples. Our approach consistently outperforms the standard OC-SVM using Radial Basis Function kernel. When there are plenty of negative samples, the binary SVM outperforms the one-class approaches as expected, but we show that for the lowest numbers of negative samples the proposed approach clearly outperforms the binary SVM.
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