Affiliation:
1. Austrian Research Institute for Al Schottengasse 3, Vienna, Austria
Abstract
We briefly describe our approach for the KDD99 Classification Cup. The solution is essentially a mixture of bagging and boosting. Additionally, asymmetric error costs are taken into account by minimizing the so-called
conditional risk
. Furthermore, the standard sampling with replacement methodology of bagging was modified to put a specific focus on the smaller but expensive-if-predicted-wrongly classes.
Publisher
Association for Computing Machinery (ACM)
Reference2 articles.
1. MetaL 1999: An ESPRIT Long-Term Research Project: A Meta-Learning Assistant for Providing User Support in Data Mining and Machine Learning http://www.cs.bris.ac.uk/cgc/METAL/ MetaL 1999: An ESPRIT Long-Term Research Project: A Meta-Learning Assistant for Providing User Support in Data Mining and Machine Learning http://www.cs.bris.ac.uk/cgc/METAL/
Cited by
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