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.
This is joint work with John Lafferty.
Back to the Main Page
Charles Rosenberg Last modified: Mon May 20 13:32:26 EDT 2002