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Selection of Negative Examples for Node Label Prediction Through Fuzzy Clustering Techniques

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Advances in Neural Networks (WIRN 2015)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 54))

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Abstract

Negative examples, which are required for most machine learning methods to infer new predictions, are rarely directly recorded in several real world databases for classification problems. A variety of heuristics for the choice of negative examples have been proposed, ranging from simply under-sampling non positive instances, to the analysis of class taxonomy structures. Here we propose an efficient strategy for selecting negative examples designed for Hopfield networks which exploits the clustering properties of positive instances. The method has been validated on the prediction of protein functions of a model organism.

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Authors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Milano, via Comelico 39, 20135, Milan, Italy

    Marco Frasca & Dario Malchiodi

Authors
  1. Marco Frasca
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  2. Dario Malchiodi
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Correspondence to Marco Frasca .

Editor information

Editors and Affiliations

  1. Computer Science Department, University of Milano, Milano, Italy

    Simone Bassis

  2. Department of Psychology, Seconda Università di Napoli and IIASS, Caserta, Italy

    Anna Esposito

  3. Dept. of Info., Mathematics, Ele & Trans, Univ. Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Francesco Carlo Morabito

  4. Dip. Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Italy

    Eros Pasero

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Frasca, M., Malchiodi, D. (2016). Selection of Negative Examples for Node Label Prediction Through Fuzzy Clustering Techniques. In: Bassis, S., Esposito, A., Morabito, F., Pasero, E. (eds) Advances in Neural Networks. WIRN 2015. Smart Innovation, Systems and Technologies, vol 54. Springer, Cham. https://doi.org/10.1007/978-3-319-33747-0_7

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  • DOI: https://doi.org/10.1007/978-3-319-33747-0_7

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-33747-0

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