Decomposing one-class support vector machine into an ensemble of one-data support vector machines
For practitioners using OCSVM on large-scale datasets, this work offers a faster alternative, though the improvement is incremental and lacks quantitative speedup or accuracy numbers.
The authors propose accelerating one-class SVM by decomposing the dataset into individual samples, training separate OCSVM models per sample, and combining them via ensemble learning, with further speedup via data reduction. The method achieves similar classification accuracy to traditional OCSVM but with faster training.
One-class classification (OCC) is a classification problem in which the training data contains only one class. The one-class support vector machine (OCSVM) is one of the most competitive OCC algorithms. However, OCSVM has scalability issues with large-scale datasets. This paper proposes the acceleration strategy of OCSVM. The idea is to decompose the dataset into samples and train OCSVM models for single data points. Subsequently, ensemble learning is applied to combine all models to compute the OCSVM model for the dataset. In addition, further acceleration is achieved through a data-reduction strategy with an OCSVM model trained on the average of the training samples. The experiment compared the proposal and traditional OCSVM using the Python package. The proposed strategy is faster than traditional OCSVM, while achieving similar classification results. Moreover, the proposed strategy can create one-to-one correspondence between samples and models. Source code is uploaded at https://github.com/ToshiHayashi/ODSVM