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International Conference on Speech and Computer

SPECOM 2022: Speech and Computer pp 464–476Cite as

Self-Configuring Genetic Programming Feature Generation in Affect Recognition Tasks

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13721))

Abstract

Feature extraction is one of the main parts of Machine Learning. Regardless of the nature of solving tasks, developers either need to use standard sets of features for a certain problem or try to generate their own features from raw data. In this paper, we present the genetic programming (GP) algorithm for feature generation issues in affect recognition tasks. We tested this approach in human affect recognition tasks on two corpora the WESAD and the RECOLA. We also used classical methods for feature space reduction Principal Component Analysis (PCA) and Independent Component Analysis (ICA). The results show the effectiveness of the GP approach in comparison with PCA and ICA and its capability to significantly reduce the feature space saving a high performance of classifiers in affect recognition tasks.

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Acknowledgments

Work of A. Karpov is supported by the RSF (project No. 22-11-00321).

Author information

Authors and Affiliations

  1. Ulm University, Ulm, Germany

    Danila Mamontov & Wolfgang Minker

  2. ITMO, Saint-Peterburg, Russia

    Danila Mamontov

  3. St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg, Russia

    Alexey Karpov

Authors
  1. Danila Mamontov
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  2. Wolfgang Minker
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  3. Alexey Karpov
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Corresponding author

Correspondence to Danila Mamontov .

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Editors and Affiliations

  1. Indian Institute of Technology Dharwad, Dharwad, India

    S. R. Mahadeva Prasanna

  2. St. Petersburg Federal Research Center of the Russian Academy of Sciences, St. Petersburg, Russia

    Alexey Karpov

  3. Koneru Lakshmaiah Education Foundation, Vaddeswaram, India

    K. Samudravijaya

  4. KIIT Group of Colleges, Gurugram, India

    Shyam S. Agrawal

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Mamontov, D., Minker, W., Karpov, A. (2022). Self-Configuring Genetic Programming Feature Generation in Affect Recognition Tasks. In: Prasanna, S.R.M., Karpov, A., Samudravijaya, K., Agrawal, S.S. (eds) Speech and Computer. SPECOM 2022. Lecture Notes in Computer Science(), vol 13721. Springer, Cham. https://doi.org/10.1007/978-3-031-20980-2_40

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  • DOI: https://doi.org/10.1007/978-3-031-20980-2_40

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  • Online ISBN: 978-3-031-20980-2

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