Yong Seog Kim
University of Iowa
2001. 5. 10
Ensemble methods have shown the potential to improve on the
performance of individual classifiers as long as the members of the
ensamble are sufficiently diverse. Individual classifiers have been
trained for example on selected subsets of the records or on
projections of the feature space to produce diversity. The resulting
ensembles reflect a priori decisions about how to allocate
records or features across classifiers. In this talk we propose a
meta-evolutionary approach in which both individual classifiers and
Ensembles compete for member classifiers, and are rewarded based on
their predictive performance.
Individual classifiers also evolve, competing to correctly classify
test points, and are given extra rewards for getting difficult points
right. In this way we aim to optimize ensembles
rather than form ensembles of individually-optimized classifiers. Our
preliminary results on a small number of data sets suggest that this
approach can generate ensembles that are more effective than
single classifiers and competetive with traditional ensemble methods.
In the long run, this approach will provide new insight into how
ensembles should be optimally constructed.
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Last update: May 08, 2001