Smooth Boosting for Margin-Based Ranking

Authors:
Jun-Ichi Moribe;Kohei Hatano;Eiji Takimoto;Masayuki Takeda
Affiliations:
Department of Informatics, Kyushu University,;Department of Informatics, Kyushu University,;Department of Informatics, Kyushu University,;Department of Informatics, Kyushu University,
Venue:
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Year:
2008

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Abstract

We propose a new boosting algorithm for bipartite ranking problems. Our boosting algorithm, called SoftRankBoost, is a modification of RankBoost which maintains only smooth distributions over data. SoftRankBoost provably achieves approximately the maximum soft margin over all pairs of positive and negative examples, which implies high AUC score for future data.