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Weighted Hamming Metric and KNN Classification of Nominal-Continuous Data

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Computational Science – ICCS 2023 (ICCS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14074))

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Abstract

The purpose of the article is to develop a new metric learning algorithm for combination of continuous and nominal data. We start with Euclidean metric for continuous and Hamming metric for nominal part of data. The impact of specific feature is modeled with corresponding weight in the metric definition. A new algorithm for automatic weights detection is proposed. The weighted metric is then used in the standard knn classification algorithm. Series of numerical experiments show that the algorithm can successfully classify raw, non-normalized data.

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Authors and Affiliations

  1. University of Warmia and Mazury in Olsztyn, ul. Słoneczna 54, 10-710, Olsztyn, Poland

    Aleksander Denisiuk

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  1. Aleksander Denisiuk
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Correspondence to Aleksander Denisiuk .

Editor information

Editors and Affiliations

  1. Czech Technical University in Prague, Prague, Czech Republic

    Jiří Mikyška

  2. University of Amsterdam, Amsterdam, The Netherlands

    Clélia de Mulatier

  3. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  4. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  5. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  6. University of Amsterdam, Amsterdam, The Netherlands

    Peter M.A. Sloot

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Denisiuk, A. (2023). Weighted Hamming Metric and KNN Classification of Nominal-Continuous Data. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14074. Springer, Cham. https://doi.org/10.1007/978-3-031-36021-3_31

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  • DOI: https://doi.org/10.1007/978-3-031-36021-3_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36020-6

  • Online ISBN: 978-3-031-36021-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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