A General Two-stage Multi-label Ranking Framework

Authors

  • Yanbing Xue University of Pittsburgh
  • Milos Hauskrecht University of Pittsburgh

DOI:

https://doi.org/10.32473/flairs.v34i1.128505

Keywords:

multi-label ranking

Abstract

In this paper we develop and study solutions for the multi-label ranking (MLR) problem. Briefly, the goal of multi-label ranking is not only to assign a set of relevant labels to a data instance but also to rank the labels according to their importance. To do so we propose a two-stage model that consists of: (1) a multi-label classification model that first selects an unordered set of labels for a data instance, and, (2) a label ordering model that orders the selected labels post-hoc in order of their importance. The advantage of such a model is that it can represent both the dependencies among labels, as well as, their importance. We evaluate the performance of our framework on both simulated and real-world datasets and show its improved performance compared to the existing multiple-label ranking solutions.

Author Biography

Milos Hauskrecht, University of Pittsburgh

Professor

Department of Computer Science

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Published

2021-04-18

How to Cite

Xue, Y., & Hauskrecht, M. (2021). A General Two-stage Multi-label Ranking Framework. The International FLAIRS Conference Proceedings, 34. https://doi.org/10.32473/flairs.v34i1.128505

Issue

Section

Main Track Proceedings