Decomposing one-class support vector machine into an ensemble of one-data support vector machines
arXiv:2606.16002v1 Announce Type: new 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 a
The continuous growth of data in various applications necessitates more scalable and efficient machine learning algorithms, making improvements to OCCSVM timely.
This development addresses a critical limitation of OCSVM by improving its scalability, potentially broadening its applicability in anomaly detection and cybersecurity for large datasets.
The computational burden of applying OCSVM to large datasets is significantly reduced, making this powerful anomaly detection method more practical for real-world, big-data scenarios.
- · AI researchers
- · Cybersecurity sector
- · Data scientists
- · Anomaly detection software providers
- · Developers reliant on less scalable OCC algorithms
More efficient anomaly detection systems can be deployed across industries with large data streams.
Improved anomaly detection leads to faster identification of security breaches or system failures, enhancing overall system robustness.
The acceleration of OCSVM could foster new applications for one-class classification in areas previously limited by computational overhead, potentially spurring innovation in specialized AI applications.
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