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Method of weak classifiers fuzzy boosting: Iterative learning of quasi-linear algorithmic composition

  • Mathematical Method in Pattern Recognition
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Abstract

Method of fuzzy boosting providing iterative weak classifiers selection and their quasi-linear composition construction is presented. The method is based on the combination of boosting and fuzzy integrating techniques, when at each step of boosting weak classifiers are combined by Choquet fuzzy integral. In the proposed FuzzyBoost algorithm 2-additive fuzzy measures were used, and method for their estimation was proposed. Although detailed theoretical verification of proposed algorithm is still absent, the experimental results, made on simulated data models, demonstrate that in the case of complex decision boundaries FuzzyBoost significantly outperforms AdaBoost.

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Authors and Affiliations

  1. Chair for Biomedical Technical Systems, Faculty of Biomedical Technique, Bauman Moscow State Technical University, Moscow, Russia

    A. V. Samorodov

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  1. A. V. Samorodov
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Correspondence to A. V. Samorodov.

Additional information

This paper uses the materials of the report submitted at the 9th Open German-Russian Workshop on Pattern Recognition and Image Understanding, held in Koblenz, December 1–5, 2014 (OGRW-9-2014).

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Samorodov Andrey Vladimirovich born on November 19, 1975. Graduated from Bauman Moscow State Technical University in 1999. Received candidate (PhD) degree in engineering sciences at Bauman Moscow State Technical University in 2002. Associate professor, Chair for Biomedical Technical Systems, Bauman Moscow State Technical University. Fields of research priorities:

Methods and algorithms of pattern recognition and multi-classification

Automated microscopy of biomedical preparations

Computer-aided decision-making systems in medicine

Methods and technique for biomedical images and signals recognition

Author of more than 200 scientific publications (including 1 collective monograph and more than 30 journal papers).

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Samorodov, A.V. Method of weak classifiers fuzzy boosting: Iterative learning of quasi-linear algorithmic composition. Pattern Recognit. Image Anal. 26, 266–273 (2016). https://doi.org/10.1134/S105466181602019X

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  • DOI: https://doi.org/10.1134/S105466181602019X

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