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Ensembles of Bayesian Network Classifiers Using Glaucoma Data and Expertise

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Ensembles in Machine Learning Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 373))

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Abstract

Bayesian Networks (BNs) are probabilistic graphical models that are popular in numerous fields. Here we propose these models to improve the classification of glaucoma, a major cause of blindness worldwide. We use visual field and retinal data to predict the early onset of glaucoma. In particular, the ability of BNs to deal with missing data allows us to select an optimal data-driven network by comparing supervised and semi-supervised models. An expertise-driven BN is also built by encoding expert knowledge in terms of relations between variables. In order to improve the overall performances for classification and to explore the relations between glaucomatous data and expert knowledge, the expertise-driven network is combined with the selected data-driven network using a BN-based approach. An accuracy-weighted combination of these networks is also compared to the other models. The best performances are obtained with the semi-supervised data-driven network. However, combining it with the expertise-driven network improves performance in many cases and leads to interesting insights about the datasets, networks and metrics.

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Authors and Affiliations

  1. Department of Information Systems and Computing, Brunel University, Uxbridge, UB8 3PH, London, UK

    Stefano Ceccon, David Garway-Heath, David Crabb & Allan Tucker

Authors
  1. Stefano Ceccon
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  2. David Garway-Heath
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  3. David Crabb
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  4. Allan Tucker
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Editor information

Editors and Affiliations

  1. University of Malmo, Stora Trädgårdsgatan 20, läg 1601, 21128, Malmö, Sweden

    Oleg Okun

  2. Department of Computer Science, University of Milan, Via Comelico 39, 20135, Milano, Italy

    Giorgio Valentini

  3. Department of Computer Science, University of Milan, via Comelico 39/41, 20135, Milano, Italia

    Matteo Re

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© 2011 Springer-Verlag Berlin Heidelberg

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Ceccon, S., Garway-Heath, D., Crabb, D., Tucker, A. (2011). Ensembles of Bayesian Network Classifiers Using Glaucoma Data and Expertise. In: Okun, O., Valentini, G., Re, M. (eds) Ensembles in Machine Learning Applications. Studies in Computational Intelligence, vol 373. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22910-7_8

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  • DOI: https://doi.org/10.1007/978-3-642-22910-7_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22909-1

  • Online ISBN: 978-3-642-22910-7

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