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Introducing Bidirectional Ordinal Classifier Cascades Based on a Pain Intensity Recognition Scenario

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Book cover Pattern Recognition. ICPR International Workshops and Challenges (ICPR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12666))

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Abstract

Ordinal classifier cascades (OCCs) are popular machine learning tools in the area of ordinal classification. OCCs constitute specific classification ensemble schemes that work in sequential manner. Each of the ensemble’s members either provides the architecture’s final prediction, or moves the current input to the next ensemble member. In the current study, we first confirm the fact that the direction of OCCs can have a high impact on the distribution of its predictions. Subsequently, we introduce and analyse our proposed bidirectional combination of OCCs. More precisely, based on a person-independent pain intensity scenario, we provide an ablation study, including the evaluation of different OCCs, as well as different popular error correcting output codes (ECOC) models. The provided outcomes show that our proposed straightforward approach significantly outperforms common OCCs, with respect to the accuracy and mean absolute error performance measures. Moreover, our results indicate that, while our proposed bidirectional OCCs are less complex in general, they are able to compete with and even outperform most of the analysed ECOC models.

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Notes

  1. 1.

    Online available at http://www.iikt.ovgu.de/BioVid.print.

  2. 2.

    More details at https://www.medoc-web.com/pathway.

  3. 3.

    www.mathworks.com.

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Acknowledgments

The work of Friedhelm Schwenker and Peter Bellmann is supported by the project Multimodal recognition of affect over the course of a tutorial learning experiment (SCHW623/7-1), funded by the German Research Foundation (DFG). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research. Hans A. Kestler acknowledges funding from the German Science Foundation (DFG, 217328187 (SFB 1074) and 288342734 (GRK HEIST)). Hans A. Kestler also acknowledges funding from the German Federal Ministery of Education and Research (BMBF) e:MED confirm (id 01ZX1708C) and TRAN-SCAN VI - PMTR-pNET (id 01KT1901B).

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Authors and Affiliations

  1. Institute of Neural Information Processing, Ulm University, James-Franck-Ring, 89081, Ulm, Germany

    Peter Bellmann & Friedhelm Schwenker

  2. Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, 89081, Ulm, Germany

    Ludwig Lausser & Hans A. Kestler

Authors
  1. Peter Bellmann
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  2. Ludwig Lausser
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  3. Hans A. Kestler
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  4. Friedhelm Schwenker
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Corresponding author

Correspondence to Peter Bellmann .

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Editors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Alberto Del Bimbo

  2. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Rita Cucchiara

  3. Department of Computer Science, Boston University, Boston, MA, USA

    Stan Sclaroff

  4. Dipartimento di Matematica e Informatica, University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Cloud & AI, JD.COM, Beijing, China

    Tao Mei

  6. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Marco Bertini

  7. Computational Sciences Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla, Puebla, Mexico

    Hugo Jair Escalante

  8. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Roberto Vezzani

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Bellmann, P., Lausser, L., Kestler, H.A., Schwenker, F. (2021). Introducing Bidirectional Ordinal Classifier Cascades Based on a Pain Intensity Recognition Scenario. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12666. Springer, Cham. https://doi.org/10.1007/978-3-030-68780-9_58

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  • DOI: https://doi.org/10.1007/978-3-030-68780-9_58

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  • Print ISBN: 978-3-030-68779-3

  • Online ISBN: 978-3-030-68780-9

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