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Frankenstein Classifiers: Some experiments on the Sisyphus data set
and J. Struyf.
In Christophe Giraud-Carrier,
and Steve Moyle, editors,
ECML/PKDD'01 workshop on Integrating Aspects of Data Mining, Decision
Support and Meta-Learning, pages 1--12. ECML/PKDD'01 workshop notes,
September 2001. More behind this link.
We present some empirical results on the use of two methods for integrating
different classifiers into a hybrid classifier that should perform better
than each of its constituent classifiers. The main point of these methods is
that instead of combining full classifiers, they combine pieces of them. One
of the methods is based on ROC analysis; the other is based on augmenting the
data with features derived from earlier learned classifiers. Experimental
results are presented that suggest that these approaches can yield
improvements over combining full classifiers.
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