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Ensemble of Binary Classifiers Combined Using Recurrent Correlation Associative Memories

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Intelligent Systems (BRACIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12320))

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Abstract

An ensemble method should cleverly combine a group of base classifiers to yield an improved classifier. The majority vote is an example of a methodology used to combine classifiers in an ensemble method. In this paper, we propose to combine classifiers using an associative memory model. Precisely, we introduce ensemble methods based on recurrent correlation associative memories (RCAMs) for binary classification problems. We show that an RCAM-based ensemble classifier can be viewed as a majority vote classifier whose weights depend on the similarity between the base classifiers and the resulting ensemble method. More precisely, the RCAM-based ensemble combines the classifiers using a recurrent consult and vote scheme. Furthermore, computational experiments confirm the potential application of the RCAM-based ensemble method for binary classification problems.

This work was supported in part by CNPq under grant no. 310118/2017-4, FAPESP under grant no. 2019/02278-2, and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

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Authors and Affiliations

  1. University of Campinas, Campinas, Brazil

    Rodolfo Anibal Lobo & Marcos Eduardo Valle

Authors
  1. Rodolfo Anibal Lobo
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  2. Marcos Eduardo Valle
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Correspondence to Rodolfo Anibal Lobo .

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Editors and Affiliations

  1. Federal University of São Carlos, São Carlos, Brazil

    Ricardo Cerri

  2. Federal University of ABC, Santo Andre, Brazil

    Ronaldo C. Prati

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Lobo, R.A., Valle, M.E. (2020). Ensemble of Binary Classifiers Combined Using Recurrent Correlation Associative Memories. In: Cerri, R., Prati, R.C. (eds) Intelligent Systems. BRACIS 2020. Lecture Notes in Computer Science(), vol 12320. Springer, Cham. https://doi.org/10.1007/978-3-030-61380-8_30

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  • DOI: https://doi.org/10.1007/978-3-030-61380-8_30

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  • Online ISBN: 978-3-030-61380-8

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