A Flexible Class of Dependence-sensitive Multi-label Loss Functions
| Type of publication: | Article |
| Citation: | wever22mlclosses |
| Journal: | Machine Learning Journal |
| Year: | 2022 |
| Note: | Accepted at the European Conference of Machine Learning (ECML) Journal Track |
| DOI: | 10.1007/s10994-021-06107-2 |
| URL: | https://arxiv.org/abs/2011.00792 |
| Abstract: | Multi-label classification is the task of assigning a subset of labels to a given query instance. For evaluating such predictions, the set of predicted labels needs to be compared to the ground-truth label set associated with that instance, and various loss functions have been proposed for this purpose. In addition to assessing predictive accuracy, a key concern in this regard is to foster and to analyze a learner's ability to capture label dependencies. In this paper, we introduce a new class of loss functions for multi-label classification, which overcome disadvantages of commonly used losses such as Hamming and subset 0/1. 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 - by giving full importance to single labels and the entire label set, respectively, Hamming and subset 0/1 are rather extreme in this regard. We present concrete instantiations of this class, which comprise Hamming and subset 0/1 as special cases, and 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. |
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