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Creating Effective Error Correcting Output Codes for Multiclass Classification

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Hybrid Artificial Intelligent Systems (HAIS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9121))

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Abstract

The error correcting output code (ECOC) technique is a genesral framework to solve the multi-class problems using binary classifiers. The key problem in this approach is how to construct the optimal ECOC codewords i.e. the codewords which maximize the recognition ratio of the final classifier. There are several methods described in the literature to solve this problem. All these methods try to maximize the minimal Hamming distance between the generated codewords. In this paper we are showing another approach based both on the average Hamming distance and the estimated misclassification error of the binary classifiers.

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Authors and Affiliations

  1. Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Krakow, Poland

    Wiesław Chmielnicki

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  1. Wiesław Chmielnicki
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Correspondence to Wiesław Chmielnicki .

Editor information

Editors and Affiliations

  1. University of Deusto, Bilbao, Spain

    Enrique Onieva

  2. University of Deusto, Bilbao, Spain

    Igor Santos

  3. University of Deusto, Bilbao, Spain

    Eneko Osaba

  4. Universidad de Salamanca, Salamanca, Spain

    Héctor Quintián

  5. Universidad de Salamanca, Salamanca, Spain

    Emilio Corchado

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Chmielnicki, W. (2015). Creating Effective Error Correcting Output Codes for Multiclass Classification. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2015. Lecture Notes in Computer Science(), vol 9121. Springer, Cham. https://doi.org/10.1007/978-3-319-19644-2_42

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  • DOI: https://doi.org/10.1007/978-3-319-19644-2_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19643-5

  • Online ISBN: 978-3-319-19644-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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