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Augment Small Training Sets Using Matching-Graphs

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Pattern Recognition and Artificial Intelligence (ICPRAI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13364))

Abstract

Both data access and data acquisition have become increasingly easy over the past decade, leading to rapid developments in many areas of intelligent information processing. In many cases, the underlying data is complex, making vectorial structures rather inappropriate for data representation. In these cases graphs provide a versatile alternative to purely numerical approaches. Regardless the representation formalism actually used, it is inevitable for supervised pattern recognition algorithms to have access to large sets of labeled training samples. In some cases, however, this requirement cannot be met because the set of labeled samples is inherently limited. In a recent research project a novel encoding of pairwise graph matchings is introduced. The basic idea of this encoding is to formalize the stable cores of pairs of patterns by means of graphs, termed matching-graphs. In the present paper we propose a novel scenario for the use of these matching-graphs. That is, we employ them to enlarge small training sets of graphs in order to stabilize the training of a classifier. In an experimental evaluation on four graph data sets we show that this novel augmentation technique improves the classification accuracy of an SVM classifier with statistical significance.

Supported by Swiss National Science Foundation (SNSF) Project Nr. 200021\(\_\)188496.

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Notes

  1. 1.

    https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets.

  2. 2.

    The statistical significance is computed via Z-test using a significance level of \(\alpha = 0.05\).

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Authors and Affiliations

  1. Institute of Computer Science, University of Bern, 3012, Bern, Switzerland

    Mathias Fuchs & Kaspar Riesen

  2. Institute for Informations Systems, University of Applied Sciences Northwestern Switzerland, 4600, Olten, Switzerland

    Kaspar Riesen

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  1. Mathias Fuchs
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Correspondence to Mathias Fuchs .

Editor information

Editors and Affiliations

  1. Télécom SudParis, Palaiseau, France

    Mounîm El Yacoubi

  2. École de Technologie Supérieure, Montreal, QC, Canada

    Eric Granger

  3. Hong Kong Baptist University, Kowloon, Kowloon, Hong Kong

    Pong Chi Yuen

  4. Indian Statistical Institute, Kolkata, India

    Umapada Pal

  5. Université Paris Cité, Paris, France

    Nicole Vincent

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Fuchs, M., Riesen, K. (2022). Augment Small Training Sets Using Matching-Graphs. In: El Yacoubi, M., Granger, E., Yuen, P.C., Pal, U., Vincent, N. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2022. Lecture Notes in Computer Science, vol 13364. Springer, Cham. https://doi.org/10.1007/978-3-031-09282-4_29

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  • DOI: https://doi.org/10.1007/978-3-031-09282-4_29

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  • Online ISBN: 978-3-031-09282-4

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