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2002
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Cranking: Combining Rankings Using Conditional Probability Models on Permutations

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Cranking: Combining Rankings Using Conditional Probability Models on Permutations
A new approach to ensemble learning is introduced that takes ranking rather than classification as fundamental, leading to models on the symmetric group and its cosets. The approach uses a generalization of the Mallows model on permutations to combine multiple input rankings. Applications include the task of combining the output of multiple search engines and multiclass or multilabel classification, where a set of input classifiers is viewed as generating a ranking of class labels. Experiments for both types of applications are presented.
Guy Lebanon, John D. Lafferty
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2002
Where ICML
Authors Guy Lebanon, John D. Lafferty
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