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In this paper, we present an
integration of the algorithm MLC4.5 for learning meta decision trees (MDTs)
into the Weka data mining suite. MDTs are a method for combining multiple
classifiers. Instead of giving a prediction, MDT leaves specify which
classifier should be used to obtain a prediction. The algorithm is based on
the C4.5 algorithm for learning ordinary decision trees. An extensive
performance evaluation of stacking with MDTs on twenty-one data sets has
been performed. We combine base-level classifiers generated by three
learning algorithms: an algorithm for learning decision trees, a nearest
neighbor algorithm and a naive Bayes algorithm. We compare MDTs to bagged
and boosted decision trees, and to combined classifiers with voting and
three different stacking methods: with ordinary decision trees, with naive
Bayes algorithm and with multi-response linear regression as a meta-level
classifier. In terms of performance, stacking with MDTs gives better
results than other methods except when compared to stacking with multi-
response linear regression as a meta-level classifier; the latter is
slightly better than MDTs.
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