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Fast Supervised Selection of Prototypes for Metric-Based Learning

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Artificial Neural Networks and Machine Learning – ICANN 2018 (ICANN 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11140))

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Abstract

A crucial factor for successful learning is the finding of more convenient representations for a problem, such that subsequent processing can be delivered to linear or non-linear modeling methods. Similarity functions are a flexible way to express knowledge about a problem and to capture meaningful relations of data in input space. In this paper we use similarity functions to find an alternative data representation which is then reduced by selecting a subset of relevant prototypes, in a supervised way. The idea is tested in a set of modelling problems, characterized by a mixture of data types and different amounts of missing values. The results demonstrate competitive or better performance than traditional methods in terms of prediction error and sparsity of the representation.

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Notes

  1. 1.

    For example, by the presence of missing values, by the feature semantics, etc.

  2. 2.

    Such variables are increasingly common, especially when they refer to a time periodicity, such as the month in a year.

  3. 3.

    It is not difficult to check that this is equivalent to the replacement of the missing similarities by the average of the non-missing ones. Therefore, the conjecture is that the missing values, if known, would not change the overall similarity significantly.

  4. 4.

    This property is not used in this work but it is interesting in other contexts, such as optimization.

  5. 5.

    The experiments were run on a HP laptop with 2GB of RAM and an Intel(R) Core(TM)2 Duo CPU T7500 at 2.20GHz.

  6. 6.

    See the caption of Table 1 for a description.

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Authors and Affiliations

  1. Computer Science Department, Technical University of Catalonia, Jordi Girona, 1-3, 08034, Barcelona, Catalonia, Spain

    Lluís A. Belanche

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  1. Lluís A. Belanche
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Correspondence to Lluís A. Belanche .

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Editors and Affiliations

  1. Czech Academy of Sciences, Prague 8, Czech Republic

    Věra Kůrková

  2. Open University of Cyprus, Latsia, Cyprus

    Yannis Manolopoulos

  3. CITEC Bielefeld University, Bielefeld, Germany

    Barbara Hammer

  4. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  5. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

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Belanche, L.A. (2018). Fast Supervised Selection of Prototypes for Metric-Based Learning. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11140. Springer, Cham. https://doi.org/10.1007/978-3-030-01421-6_55

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  • DOI: https://doi.org/10.1007/978-3-030-01421-6_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01420-9

  • Online ISBN: 978-3-030-01421-6

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