PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Reverse Multi-Label Learning
James Petterson and Tiberio Caetano
In: NIPS, 6-11 Dec 2010, Vancouver, Canada.


Multi-label classification is the task of predicting potentially multiple labels for a given instance. This is common in several applications such as image annotation, document classification and gene function prediction. In this paper we present a formulation for this problem based on reverse prediction: we predict sets of instances given the labels. By viewing the problem from this perspective, the most popular quality measures for assessing the performance of multi-label classification admit relaxations that can be efficiently optimised. We optimise these relaxations with standard algorithms and compare our results with several stateof-the-art methods, showing excellent performance.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:7239
Deposited By:James Petterson
Deposited On:14 March 2011