PATTERN TREE SPATIAL MODELS FOR ECOLOGICAL CLASSIFICATION
Date Issued
2012
Author(s)
Abstract
This paper further extends pattern trees membership
functions, by implementing a modified sigmoid distribution.
In this work we use this algorithm to extract knowledge for
ecological classification task from the diatoms community
measured dataset, which according the biological experts are
used as bio-indicators in many water ecosystem
environments. The first part of the algorithm transforms the
input set from crisp values into fuzzy values, and then
continues the induction of the tree. The transformation is
achieved by using different membership functions, which
have different shape and mathematical description. This is
very important because later in the induction phase this will
have effect on the classification accuracy and complexity of
the obtained model. The modified sigmoid function that is put
on test, have several advantage over the triangular and
trapezoidal functions. The experiments on diatoms
classification datasets showed that sigmoid shaped function
algorithm models outperform the pattern tree models build
based on the trapezoidal, triangular or Gaussian MF in terms
of prediction accuracy. The diatom models based on this
method produced valid and useful knowledge that later in the
paper is interpreted. Finally, evaluation performance analyses
of the build pattern trees with classical classification
algorithms is presented and discussed.
functions, by implementing a modified sigmoid distribution.
In this work we use this algorithm to extract knowledge for
ecological classification task from the diatoms community
measured dataset, which according the biological experts are
used as bio-indicators in many water ecosystem
environments. The first part of the algorithm transforms the
input set from crisp values into fuzzy values, and then
continues the induction of the tree. The transformation is
achieved by using different membership functions, which
have different shape and mathematical description. This is
very important because later in the induction phase this will
have effect on the classification accuracy and complexity of
the obtained model. The modified sigmoid function that is put
on test, have several advantage over the triangular and
trapezoidal functions. The experiments on diatoms
classification datasets showed that sigmoid shaped function
algorithm models outperform the pattern tree models build
based on the trapezoidal, triangular or Gaussian MF in terms
of prediction accuracy. The diatom models based on this
method produced valid and useful knowledge that later in the
paper is interpreted. Finally, evaluation performance analyses
of the build pattern trees with classical classification
algorithms is presented and discussed.
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