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11

ICML
2009
IEEE

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Decision tree and instance-based learning for label ranking

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Decision tree and instance-based learning for label ranking

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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

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Decision Tree Induction | ICML 2009 | Label Ranking Problem | Label Rankings | Machine Learning |

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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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