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Supervised clustering using decision trees and decision graphs: An ecological comparison

Author

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  • Dale, M.B.
  • Dale, P.E.R.
  • Tan, P.

Abstract

In this paper, we outline some of the problems in computer learning, particularly with respect to decision trees. We then consider how, in some cases, a decision graph may provide a solution to some of these problems. We compare a decision graph analysis with a decision tree analysis of salt marsh data, predicting predetermined vegetation types from environmental properties. All analyses use a minimum message length criterion to select an optimal model within a class, thereby avoiding subjective decisions. Minimum message length also provides a criterion for choosing between the model classes of tree and graph.

Suggested Citation

  • Dale, M.B. & Dale, P.E.R. & Tan, P., 2007. "Supervised clustering using decision trees and decision graphs: An ecological comparison," Ecological Modelling, Elsevier, vol. 204(1), pages 70-78.
    • Handle: RePEc:eee:ecomod:v:204:y:2007:i:1:p:70-78
    DOI: 10.1016/j.ecolmodel.2006.12.021
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    References listed on IDEAS

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    1. Ole E. Barndorff‐Nielsen & Richard D. Gill & Peter E. Jupp, 2003. "On quantum statistical inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(4), pages 775-804, November.
    2. Jonathan Oliver & David Hand, 1996. "Averaging over decision trees," Journal of Classification, Springer;The Classification Society, vol. 13(2), pages 281-297, September.
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    Cited by:

    1. Coro, Gianpaolo & Webb, Thomas J. & Appeltans, Ward & Bailly, Nicolas & Cattrijsse, André & Pagano, Pasquale, 2015. "Classifying degrees of species commonness: North Sea fish as a case study," Ecological Modelling, Elsevier, vol. 312(C), pages 272-280.

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