One-Class SVM Probabilistic Outputs

doi:10.1109/TNNLS.2024.3395148

One-Class SVM Probabilistic Outputs

2024-01-01

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AI

DOI：10.1109/TNNLS.2024.3395148

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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.

Keyword:

Support vector machines

Probabilistic logic

Standards

Optimization

Training data

Training

Gaussian distribution

One-class support vector machine (SVM)

outlier detection

Platt scaling

probability estimation

One-Class SVM Probabilistic Outputs

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