Learning to rank: a ROC-based graph-theoretic approach |
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Authors: | Willem Waegeman |
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Institution: | 1. Department of Applied Mathematics, Biometrics and Process Control, Universiteit Gent, Coupure links 653, 9000, Ghent, Belgium
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Abstract: | This note summarizes the main results presented in the author’s Ph.D. thesis, supervised by Luc Boullart and Bernard De Baets.
The thesis was defended on 14th October 2008 at Universiteit Gent. It is written in English and available for download at
. The work deals with preference learning, with emphasis on the ranking and ordinal regression machine learning settings and
their connections to decision theory. Based on receiver operator characteristics analysis and graph theory, new performance
measures are proposed to evaluate this type of models, and new algorithms are presented to compute and optimize these performance
measures efficiently. Furthermore, the relationship with other settings like pairwise preference learning and multi-class
classification is discussed. |
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Keywords: | |
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