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Novel Approach to Gentle AdaBoost Algorithm with Linear Weak Classifiers

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Abstract

This paper presents the problem of calculating the value of the scoring function for weak classifiers operating in the sequential structure. An example of such a structure is Gentle AdaBoost algorithm whose modification we propose in this work. In the proposed approach the distance of the object from the decision boundary is scaled in decision regions defined by the weak classifier at first and later transformed by the log-normal function. The described algorithm was tested on sixth public available data sets and compared with Gentle AdaBoost algorithm.

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Acknowledgment

This work was supported by the National Science Centre, Poland under the grant no. 2017/25/B/ST6/01750.

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Authors and Affiliations

  1. Faculty of Electronic, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Robert Burduk, Wojciech Bożejko & Szymon Zacher

Authors
  1. Robert Burduk
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  2. Wojciech Bożejko
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  3. Szymon Zacher
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Corresponding authors

Correspondence to Robert Burduk , Wojciech Bożejko or Szymon Zacher .

Editor information

Editors and Affiliations

  1. Department of Applied Informatics, Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Kietikul Jearanaitanakij

  3. Faculty of Computer Science and Information, University Teknologi Malaysia, Kuala Lumpur, Malaysia

    Ali Selamat

  4. Department of Applied Informatics, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  5. King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Suphamit Chittayasothorn

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Burduk, R., Bożejko, W., Zacher, S. (2020). Novel Approach to Gentle AdaBoost Algorithm with Linear Weak Classifiers. In: Nguyen, N., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Lecture Notes in Computer Science(), vol 12033. Springer, Cham. https://doi.org/10.1007/978-3-030-41964-6_52

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  • DOI: https://doi.org/10.1007/978-3-030-41964-6_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41963-9

  • Online ISBN: 978-3-030-41964-6

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