Bipartite ranking refers to the problem of learning a ranking function from a training set of positively and negatively labeled
examples. Applied to a set of unlabeled instances, a ranking function is expected to establish a total order in which positiveinstances precede negative ones. The performance of a ranking function is typically measured in terms of the AUC. In thispaper, we study the problem of multipartite ranking, an extension of bipartite ranking to the multi-class case. In this regard,we discuss extensions of the AUC metric which are suitable as evaluation criteria for multipartite rankings. Moreover, tolearn multipartite ranking functions, we propose methods on the basis of binary decomposition techniques that have previouslybeen used for multi-class and ordinal classification. We compare these methods both analytically and experimentally, not onlyagainst each other but also to existing methods applicable to the same problem.
%0 Journal Article
%1 johannes2009binary
%A Fürnkranz, Johannes
%A Hüllermeier, Eyke
%A Vanderlooy, Stijn
%D 2009
%J Machine Learning and Knowledge Discovery in Databases
%K 2009 ecml graph multipartite pkdd ranking
%P 359--374
%T Binary Decomposition Methods for Multipartite Ranking
%U http://dx.doi.org/10.1007/978-3-642-04180-8_41
%X Bipartite ranking refers to the problem of learning a ranking function from a training set of positively and negatively labeled
examples. Applied to a set of unlabeled instances, a ranking function is expected to establish a total order in which positiveinstances precede negative ones. The performance of a ranking function is typically measured in terms of the AUC. In thispaper, we study the problem of multipartite ranking, an extension of bipartite ranking to the multi-class case. In this regard,we discuss extensions of the AUC metric which are suitable as evaluation criteria for multipartite rankings. Moreover, tolearn multipartite ranking functions, we propose methods on the basis of binary decomposition techniques that have previouslybeen used for multi-class and ordinal classification. We compare these methods both analytically and experimentally, not onlyagainst each other but also to existing methods applicable to the same problem.
@article{johannes2009binary,
abstract = {Bipartite ranking refers to the problem of learning a ranking function from a training set of positively and negatively labeled
examples. Applied to a set of unlabeled instances, a ranking function is expected to establish a total order in which positiveinstances precede negative ones. The performance of a ranking function is typically measured in terms of the AUC. In thispaper, we study the problem of multipartite ranking, an extension of bipartite ranking to the multi-class case. In this regard,we discuss extensions of the AUC metric which are suitable as evaluation criteria for multipartite rankings. Moreover, tolearn multipartite ranking functions, we propose methods on the basis of binary decomposition techniques that have previouslybeen used for multi-class and ordinal classification. We compare these methods both analytically and experimentally, not onlyagainst each other but also to existing methods applicable to the same problem.},
added-at = {2009-09-09T14:42:12.000+0200},
author = {Fürnkranz, Johannes and Hüllermeier, Eyke and Vanderlooy, Stijn},
biburl = {https://www.bibsonomy.org/bibtex/2472363e85298f4d6e188d92a4319918a/folke},
description = {SpringerLink - Book Chapter},
interhash = {780e00a583e280eebfa4d87cc74e62a1},
intrahash = {472363e85298f4d6e188d92a4319918a},
journal = {Machine Learning and Knowledge Discovery in Databases},
keywords = {2009 ecml graph multipartite pkdd ranking},
pages = {359--374},
timestamp = {2009-09-09T14:42:13.000+0200},
title = {Binary Decomposition Methods for Multipartite Ranking},
url = {http://dx.doi.org/10.1007/978-3-642-04180-8_41},
year = 2009
}