A flexible class of dependence-aware multi-label loss functions

verfasst von: Eyke Hüllermeier, Marcel Wever, Eneldo Loza Mencia, Johannes Fürnkranz, Michael Rapp
Abstract: The idea to exploit label dependencies for better prediction is at the core of methods for multi-label classification (MLC), and performance improvements are normally explained in this way. Surprisingly, however, there is no established methodology that allows to analyze the dependence-awareness of MLC algorithms. With that goal in mind, we introduce a class of loss functions that are able to capture the important aspect of label dependence. To this end, we leverage the mathematical framework of non-additive measures and integrals. Roughly speaking, a non-additive measure allows for modeling the importance of correct predictions of label subsets (instead of single labels), and thereby their impact on the overall evaluation, in a flexible way. The well-known Hamming and subset 0/1 losses are rather extreme special cases of this function class, which give full importance to single label sets or the entire label set, respectively. We present concrete instantiations of this class, which appear to be especially appealing from a modeling perspective. The assessment of multi-label classifiers in terms of these losses is illustrated in an empirical study, clearly showing their aptness at capturing label dependencies. Finally, while not being the main goal of this study, we also show some preliminary results on the minimization of this parametrized family of losses.
Externe Organisation(en): Ludwig-Maximilians-Universität München (LMU)
Universität Paderborn
Technische Universität Darmstadt
Johannes Kepler Universität Linz (JKU)
Typ: Artikel
Journal: Machine learning
Band: 111
Seiten: 713-737
Anzahl der Seiten: 25
ISSN: 0885-6125
Publikationsdatum: 02.2022
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Software, Artificial intelligence
Elektronische Version(en): https://doi.org/10.1007/s10994-021-06107-2 (Zugang: Offen)

BibTeX

@article{1ffc80d7ee3243e0bf554778c51fb083,
title = "A flexible class of dependence-aware multi-label loss functions",
abstract = "The idea to exploit label dependencies for better prediction is at the core of methods for multi-label classification (MLC), and performance improvements are normally explained in this way. Surprisingly, however, there is no established methodology that allows to analyze the dependence-awareness of MLC algorithms. With that goal in mind, we introduce a class of loss functions that are able to capture the important aspect of label dependence. To this end, we leverage the mathematical framework of non-additive measures and integrals. Roughly speaking, a non-additive measure allows for modeling the importance of correct predictions of label subsets (instead of single labels), and thereby their impact on the overall evaluation, in a flexible way. The well-known Hamming and subset 0/1 losses are rather extreme special cases of this function class, which give full importance to single label sets or the entire label set, respectively. We present concrete instantiations of this class, which appear to be especially appealing from a modeling perspective. The assessment of multi-label classifiers in terms of these losses is illustrated in an empirical study, clearly showing their aptness at capturing label dependencies. Finally, while not being the main goal of this study, we also show some preliminary results on the minimization of this parametrized family of losses.",
keywords = "Analysis, Label dependence, Loss function, Multi-label classification, Non-additive measures",
author = "Eyke H{\"u}llermeier and Marcel Wever and {Loza Mencia}, Eneldo and Johannes F{\"u}rnkranz and Michael Rapp",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s).",
year = "2022",
month = feb,
doi = "10.1007/s10994-021-06107-2",
language = "English",
volume = "111",
pages = "713--737",
journal = "Machine learning",
issn = "0885-6125",
publisher = "Springer Netherlands",
number = "2",
}

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A flexible class of dependence-aware multi-label loss functions

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