Technical Reports - Query Results
Your query term was 'number = 2002-20'1 report found
- ÖFAI-TR-2002-20 (
212kB PDF file)Pairwise Classification as an Ensemble Technique
- Johannes Fürnkranz
- In this paper we investigate the performance of pairwise (or round
robin) classification, originally a technique for turning
multi-class problems into two-class problems, as a general ensemble
technique. In particular, we show that the use of round robin
ensembles will also increase the classification performance of
decision tree learners, which could directly handle multi-class
problems. The performance gain is not as large as for bagging and
boosting, but on the other hand round robin ensembles have a clear
semantics. Furthermore, we show that the advantage of pairwise
classification over direct multi-class classification and
one-against-all binarization increases with the number of classes,
and that round robin ensembles form an interesting alternative for
problems with ordered class values.
Keywords: Class Binarization, Pairwise Classification, Round Robin Learning, Inductive Rule Learning, Ensemble Methods
- In this paper we investigate the performance of pairwise (or round
robin) classification, originally a technique for turning
multi-class problems into two-class problems, as a general ensemble
technique. In particular, we show that the use of round robin
ensembles will also increase the classification performance of
decision tree learners, which could directly handle multi-class
problems. The performance gain is not as large as for bagging and
boosting, but on the other hand round robin ensembles have a clear
semantics. Furthermore, we show that the advantage of pairwise
classification over direct multi-class classification and
one-against-all binarization increases with the number of classes,
and that round robin ensembles form an interesting alternative for
problems with ordered class values.