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Gentle AdaBoost Algorithm with Score Function Dependent on the Distance to Decision Boundary

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Computer Information Systems and Industrial Management (CISIM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11703))

Abstract

This paper presents a new extension of Gentle AdaBoost algorithm based on the distance of the object to the decision boundary, which is defined by the weak classifier used in boosting. In the proposed approach this distance is transformed by Gaussian function and defines the value of a score function. The assumed form of transforming functions means that the objects closest or farthest located from the decision boundary of the basic classifier have the lowest value of the scoring function. The described algorithm was tested on four data sets from UCI repository and compared with Gentle AdaBoost algorithm.

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Acknowledgments

This work was supported in part 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 Bozejko

Authors
  1. Robert Burduk
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  2. Wojciech Bozejko
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Corresponding authors

Correspondence to Robert Burduk or Wojciech Bozejko .

Editor information

Editors and Affiliations

  1. Bialystok University of Technology, Bialystok, Poland

    Khalid Saeed

  2. University of Calcutta, Calcutta, India

    Rituparna Chaki

  3. Mihajlo Pupin Institute, Belgrade, Serbia

    Valentina Janev

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Burduk, R., Bozejko, W. (2019). Gentle AdaBoost Algorithm with Score Function Dependent on the Distance to Decision Boundary. In: Saeed, K., Chaki, R., Janev, V. (eds) Computer Information Systems and Industrial Management. CISIM 2019. Lecture Notes in Computer Science(), vol 11703. Springer, Cham. https://doi.org/10.1007/978-3-030-28957-7_25

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  • DOI: https://doi.org/10.1007/978-3-030-28957-7_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-28956-0

  • Online ISBN: 978-3-030-28957-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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