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Your query term was 'number = 2003-14'1 report found
- OFAI-TR-2003-14 (
159kB PDF file)Pairwise Preference Learning and Ranking
- Johannes Fürnkranz, Eyke Hüllermeier
- We consider supervised learning of a ranking function, which is a
mapping from instances to total orders over a set of labels
(options). The training information consists of examples
with partial (and possibly inconsistent) information about their
associated rankings. From these, we induce a ranking function by
reducing the original problem to a number of binary classification
problems, one for each pair of labels.
The main objective of this work is to investigate the trade-off between
the quality of the induced ranking function and the computational complexity
of the algorithm, both depending on the amount of preference information
given for each example. To this end, we present theoretical
results on the complexity of pairwise preference
learning. We also carry out some controlled experiments investigating the
predictive performance of our method for different types of preference
information, such as top-ranked labels and complete rankings.
The domain of this study is the prediction of a rational agent's
ranking of actions in an uncertain environment.
Keywords: Ranking, Pairwise Classification, Round Robin Learning
- We consider supervised learning of a ranking function, which is a
mapping from instances to total orders over a set of labels
(options). The training information consists of examples
with partial (and possibly inconsistent) information about their
associated rankings. From these, we induce a ranking function by
reducing the original problem to a number of binary classification
problems, one for each pair of labels.
The main objective of this work is to investigate the trade-off between
the quality of the induced ranking function and the computational complexity
of the algorithm, both depending on the amount of preference information
given for each example. To this end, we present theoretical
results on the complexity of pairwise preference
learning. We also carry out some controlled experiments investigating the
predictive performance of our method for different types of preference
information, such as top-ranked labels and complete rankings.
The domain of this study is the prediction of a rational agent's
ranking of actions in an uncertain environment.