When a multi-label classifier outputs a real-valued score for each class, a well known design strategy consists of tuning the corresponding decision thresholds by optimising the performance measure of interest on validation data. In this paper we focus on the F-measure, which is widely used in multi-label problems. We derive two properties of the micro-averaged F measure, viewed as a function of the threshold values, which allow its global maximum to be found by an optimisation strategy with an upper bound on computational complexity of O(n2N2), where N and n are respectively the number of classes and of validation samples. So far, only a suboptimal threshold selection rule and a greedy algorithm without any optimality guarantee were known for this task. We then devise a possible optimisation algorithm based on our strategy, and evaluate it on three benchmark, multi-label data sets.

F-measure optimisation in multi-label classifiers

ROLI, FABIO
2012

Abstract

When a multi-label classifier outputs a real-valued score for each class, a well known design strategy consists of tuning the corresponding decision thresholds by optimising the performance measure of interest on validation data. In this paper we focus on the F-measure, which is widely used in multi-label problems. We derive two properties of the micro-averaged F measure, viewed as a function of the threshold values, which allow its global maximum to be found by an optimisation strategy with an upper bound on computational complexity of O(n2N2), where N and n are respectively the number of classes and of validation samples. So far, only a suboptimal threshold selection rule and a greedy algorithm without any optimality guarantee were known for this task. We then devise a possible optimisation algorithm based on our strategy, and evaluate it on three benchmark, multi-label data sets.
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: http://hdl.handle.net/11567/1093817
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 24
  • ???jsp.display-item.citation.isi??? 19
social impact