Decision Tree and Instance-Based Learning for Label Ranking

author: Weiwei Cheng, Mathematik und Informatik, Philipps-Universität Marburg
published: Aug. 26, 2009,   recorded: June 2009,   views: 9551


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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.

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Comment1 小惠, September 15, 2009 at 3:46 a.m.:

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Comment2 Amanda, November 25, 2020 at 1:18 p.m.:

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