Tree Kernel Usage in Naive Bayes Classifiers
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Date
2012-02-21
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Abstract
We present a novel approach in machine learning
by combining naive Bayes classifiers with tree
kernels. Tree kernel methods produce promising
results in machine learning tasks containing treestructured
attribute values. These kernel methods
are used to compare two tree-structured attribute
values recursively. Up to now tree kernels are
only used in kernel machines like Support Vector
Machines or Perceptrons.
In this paper, we show that tree kernels can be
utilized in a naive Bayes classifier enabling the
classifier to handle tree-structured values. We
evaluate our approach on three datasets containing
tree-structured values. We show that our
approach using tree-structures delivers significantly
better results in contrast to approaches using
non-structured (flat) features extracted from
the tree. Additionally, we show that our approach
is significantly faster than comparable kernel machines
in several settings which makes it more
useful in resource-aware settings like mobile devices.
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Keywords
Lazy Learning, Naive Bayes Classifier, Tree Kernel, Tree-structured Values