Abstract
The Support Vector Machine (SVM) is a widely used algorithm for batch classification with a run and memory efficient counterpart given by the Core Vector Machine (CVM). Both algorithms have nice theoretical guarantees, but are not able to handle data streams, which have to be processed instance by instance. We propose a novel approach to handle stream classification problems via an adaption of the CVM, which is also able to handle multiclass classification problems. Furthermore, we compare our Multiclass Core Vector Machine (MCCVM) approach against another existing Minimum Enclosing Ball (MEB)-based classification approach. Finally, we propose a real-world streaming dataset, which consists of changeover detection data and has only been analyzed in offline settings so far.
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Notes
- 1.
Code can be found at https://github.com/foxriver76/SW-MEB-Python.
- 2.
The dataset can be found on https://github.com/ValdsteiN/OBerA-Enhanced-Changeover-Detection-in-Industry-4.0-environments-with-Machine-Learning.
- 3.
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Acknowledgement
We are thankful for support in the mFUND program of the BMVI, project FlowPro, grant number 19F2128B.
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Heusinger, M., Schleif, FM. (2023). A Streaming Approach to the Core Vector Machine. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2022. Lecture Notes in Computer Science(), vol 13589. Springer, Cham. https://doi.org/10.1007/978-3-031-23480-4_8
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