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

    Author(s)
    Dale, MB
    Dale, PER
    Tan, P
    Griffith University Author(s)
    Dale, Patricia E.
    Year published
    2007
    Metadata
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    Abstract
    n 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. In addition ...
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    n 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. In addition to the computational evaluation of models, we examine the ecological implications of the selected solutions. Even if sub-optimal, it is possible that a result can contribute to understanding of the underlying real system.
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    Journal Title
    Ecological Modelling
    Volume
    204
    Issue
    1-2
    Publisher URI
    http://www.elsevier.com/locate/ecolmodel
    DOI
    https://doi.org/10.1016/j.ecolmodel.2006.12.021
    Copyright Statement
    © 2007 Elsevier. Please refer to the journal's website for access to the definitive, published version.
    Subject
    Multi-Disciplinary
    Publication URI
    http://hdl.handle.net/10072/16523
    Collection
    • Journal articles

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