One-Class SVM Probabilistic Outputs
Zhongyi Que, Chih‐Jen Lin
Abstract
One-class support vector machine (SVM) is an extension of SVM to handle unlabeled data. As a mature technique for outlier detection, one-class SVM has been widely used in many applications. However, similar to standard two-class SVM, the design of one-class SVM does not give probabilistic outputs. For two-class SVM, some methods have been proposed to effectively obtain probabilistic outputs, but due to the difficulty of no-label information, less attention has been paid to one-class SVM. Our aim in this work is to propose some practically viable techniques to generate probabilistic outputs for one-class SVM. We investigate existing methods for two-class SVM and explain why they may not be suitable for one-class SVM. Due to the lack of label information, we think a feasible setting is to have probabilities mimic to the decision values of training data. Based on this principle, we propose several new methods. Detailed experiments on both artificial and real-world data demonstrate the effectiveness of the proposed methods.