Details
| Original language | English |
|---|---|
| Pages (from-to) | 713-737 |
| Number of pages | 25 |
| Journal | Machine learning |
| Volume | 111 |
| Issue number | 2 |
| Publication status | Published - Feb 2022 |
| Externally published | Yes |
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
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Artificial Intelligence
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In: Machine learning, Vol. 111, No. 2, 02.2022, p. 713-737.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A flexible class of dependence-aware multi-label loss functions
AU - Hüllermeier, Eyke
AU - Wever, Marcel
AU - Loza Mencia, Eneldo
AU - Fürnkranz, Johannes
AU - Rapp, Michael
N1 - Publisher Copyright: © 2022, The Author(s).
PY - 2022/2
Y1 - 2022/2
N2 - 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.
AB - 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.
KW - Analysis
KW - Label dependence
KW - Loss function
KW - Multi-label classification
KW - Non-additive measures
UR - http://www.scopus.com/inward/record.url?scp=85123113719&partnerID=8YFLogxK
U2 - 10.1007/s10994-021-06107-2
DO - 10.1007/s10994-021-06107-2
M3 - Article
AN - SCOPUS:85123113719
VL - 111
SP - 713
EP - 737
JO - Machine learning
JF - Machine learning
SN - 0885-6125
IS - 2
ER -