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A Streaming Approach to the Core Vector Machine

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Artificial Intelligence and Soft Computing (ICAISC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13589))

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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. 1.

    Code can be found at https://github.com/foxriver76/SW-MEB-Python.

  2. 2.

    The dataset can be found on https://github.com/ValdsteiN/OBerA-Enhanced-Changeover-Detection-in-Industry-4.0-environments-with-Machine-Learning.

  3. 3.

    https://www.kaggle.com/c/GiveMeSomeCredit.

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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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Authors and Affiliations

  1. Department of Computer Science, UAS Würzburg-Schweinfurt, Sanderheinrichsleitenweg 20, Würzburg, Germany

    Moritz Heusinger

  2. School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK

    Frank-Michael Schleif

Authors
  1. Moritz Heusinger
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  2. Frank-Michael Schleif
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Corresponding author

Correspondence to Moritz Heusinger .

Editor information

Editors and Affiliations

  1. Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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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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  • DOI: https://doi.org/10.1007/978-3-031-23480-4_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23479-8

  • Online ISBN: 978-3-031-23480-4

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