F -Measure Optimisation in Multi-label Classifiers

TitleF -Measure Optimisation in Multi-label Classifiers
Publication TypeConference Paper
Year of Publication2014
AuthorsPillai, I, Fumera, G, Roli, F
Conference Name22nd IEEE International Conference on Pattern Recognition, ICPR 2014
Pagination3452 - 3456
Date Published08/2014
Conference LocationStockholm, Sweden
ISSN Number1051-4651

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(n2 N 2), 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.


Citation Keypillai_ICPR2014
Refereed DesignationRefereed
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