Generalized Reference Kernel With Negative Samples For Support Vector One-class Classification
This is an incremental improvement for one-class classification tasks where only a few negative samples are available, such as in anomaly detection or imbalanced datasets.
The paper tackles small-scale one-class classification with limited negative samples by proposing GRKneg, a kernel improvement for OC-SVM that uses negative data without labels, and shows it consistently outperforms standard OC-SVM and binary SVM when negative samples are scarce.
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.