Decomposing one-class support vector machine into an ensemble of one-data support vector machines

2026-06-14

AI Summary

This work addresses the poor scalability of one-class support vector machines (OCSVM) on large-scale datasets by proposing a novel decomposition-based ensemble approach. The original problem is decomposed into individual single-sample OCSVM models, whose outputs are aggregated through ensemble learning to approximate the global solution. A key innovation is the introduction of a data reduction strategy based on sample means, which significantly accelerates training while preserving classification performance. Experimental results demonstrate that the proposed method achieves substantially higher training efficiency compared to conventional OCSVM, with comparable anomaly detection accuracy. Moreover, the one-to-one correspondence established between samples and submodels enhances model interpretability.

Abstract

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

Problem

• one-class classification
• one-class support vector machine
• scalability
• large-scale datasets

Innovation

• One-class classification
• Support Vector Machine
• Ensemble Learning
• Scalability
• Data Reduction