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Predicting Rice Phenotypes with Meta-learning

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Discovery Science (DS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11198))

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Abstract

The features in some machine learning datasets can naturally be divided into groups. This is the case with genomic data, where features can be grouped by chromosome. In many applications it is common for these groupings to be ignored, as interactions may exist between features belonging to different groups. However, including a group that does not influence a response introduces noise when fitting a model, leading to suboptimal predictive accuracy. Here we present two general frameworks for the generation and combination of meta-features when feature groupings are present. We evaluated the frameworks on a genomic rice dataset where the regression task is to predict plant phenotype. We conclude that there are use cases for both frameworks.

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Author information

Authors and Affiliations

  1. The University of Manchester, Manchester, M13 9PL, United Kingdom

    Oghenejokpeme I. Orhobor & Ross D. King

  2. The International Rice Research Institute, Los Baños, Philippines

    Nickolai N. Alexandrov

Authors
  1. Oghenejokpeme I. Orhobor
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  2. Nickolai N. Alexandrov
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  3. Ross D. King
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Corresponding author

Correspondence to Oghenejokpeme I. Orhobor .

Editor information

Editors and Affiliations

  1. Goldsmiths University of London, London, UK

    Larisa Soldatova

  2. Eindhoven University of Technology, Eindhoven, The Netherlands

    Joaquin Vanschoren

  3. University of Cyprus, Nicosia, Cyprus

    George Papadopoulos

  4. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

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Orhobor, O.I., Alexandrov, N.N., King, R.D. (2018). Predicting Rice Phenotypes with Meta-learning. In: Soldatova, L., Vanschoren, J., Papadopoulos, G., Ceci, M. (eds) Discovery Science. DS 2018. Lecture Notes in Computer Science(), vol 11198. Springer, Cham. https://doi.org/10.1007/978-3-030-01771-2_10

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  • DOI: https://doi.org/10.1007/978-3-030-01771-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01770-5

  • Online ISBN: 978-3-030-01771-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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