Supervised clustering using decision trees and decision graphs: An ecological comparison.
Author(s)
Dale, MB
Dale, PER
Tan, P
Griffith University Author(s)
Year published
2007
Metadata
Show full item recordAbstract
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 ...
View more >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.
View less >
View more >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.
View less >
Journal Title
Ecological Modelling
Volume
204
Issue
1-2
Publisher URI
Copyright Statement
© 2007 Elsevier. Please refer to the journal's website for access to the definitive, published version.
Subject
Multi-Disciplinary