Technical Reports - Query Results
Your query term was 'number = 2001-02'1 report found
- OFAI-TR-2001-02 (
57kB g-zipped PostScript file, 
130kB PDF file)Round Robin Rule Learning
- Johannes Fürnkranz
- In this paper, we discuss a technique for handling multi-class
problems with binary classifiers.
The idea - learning one classifier for each pair of classes - is
known as pairwise classification but - to our knowledge - has
not yet been thoroughly investigated
in the context of inductive rule learning. We present an
empirical evaluation of the method as a wrapper around the Ripper
rule learning algorithm on 20 multi-class datasets from the UCI
database repository. Our results show that the method is very
likely to improve Ripper's classification performance without having
a high risk of
decreasing it. The size of this improvement is similar
to that obtained by boosting C5. In addition, we give a
theoretical analysis of the complexity of the approach and show that
its training time is within a small constant bound of the training
time of the sequential class learning technique that is currently
used in Ripper.
Keywords: Rule Learning, Pairwise Classification, Class Binarization
- In this paper, we discuss a technique for handling multi-class
problems with binary classifiers.
The idea - learning one classifier for each pair of classes - is
known as pairwise classification but - to our knowledge - has
not yet been thoroughly investigated
in the context of inductive rule learning. We present an
empirical evaluation of the method as a wrapper around the Ripper
rule learning algorithm on 20 multi-class datasets from the UCI
database repository. Our results show that the method is very
likely to improve Ripper's classification performance without having
a high risk of
decreasing it. The size of this improvement is similar
to that obtained by boosting C5. In addition, we give a
theoretical analysis of the complexity of the approach and show that
its training time is within a small constant bound of the training
time of the sequential class learning technique that is currently
used in Ripper.