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ICML
2009
IEEE

Decision tree and instance-based learning for label ranking

11 years 6 months ago
Decision tree and instance-based learning for label ranking
The label ranking problem consists of learning a model that maps instances to total orders over a finite set of predefined labels. This paper introduces new methods for label ranking that complement and improve upon existing approaches. More specifically, we propose extensions of two methods that have been used extensively for classification and regression so far, namely instance-based learning and decision tree induction. The unifying element of the two methods is a procedure for locally estimating predictive probability models for label rankings.
Weiwei Cheng, Jens C. Huhn, Eyke Hüllermeier
Added 19 May 2010
Updated 19 May 2010
Type Conference
Year 2009
Where ICML
Authors Weiwei Cheng, Jens C. Huhn, Eyke Hüllermeier
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