Developing learning algorithms for multilabel classification problems, when the goal is to maximizing the micro-averaged F measure, is a difficult problem for which no solution was known so far. In this paper we provide an exact solution for the case when the popular binary relevance approach is used for designing a multilabel classifier. We prove that the empirical maximum of the micro-averaged F measure can be attained by iteratively retraining class-related binary classifiers whose learning algorithm is capable of maximizing a modified version of the F measure of a two-class problem. We apply our optimization strategy to an existing formulation of support vector machine classifiers tailored to performance measures like F, and evaluate it on benchmark multilabel data sets.

Learning of multilabel classifiers

ROLI, FABIO
2014-01-01

Abstract

Developing learning algorithms for multilabel classification problems, when the goal is to maximizing the micro-averaged F measure, is a difficult problem for which no solution was known so far. In this paper we provide an exact solution for the case when the popular binary relevance approach is used for designing a multilabel classifier. We prove that the empirical maximum of the micro-averaged F measure can be attained by iteratively retraining class-related binary classifiers whose learning algorithm is capable of maximizing a modified version of the F measure of a two-class problem. We apply our optimization strategy to an existing formulation of support vector machine classifiers tailored to performance measures like F, and evaluate it on benchmark multilabel data sets.
2014
978-1-4799-5208-3
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1093478
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact