Abstract
In multi-label classification, each instance can belong to multiple labels simultaneously. Different evaluation criteria have been proposed for comparing ground-truth label sets and predictions. Probabilistic multi-label classifiers offer a unique advantage by allowing optimization of different criteria at prediction time, but they have been relatively underexplored due to a shortage of insights into inference complexity and evaluation criteria. To shrink this gap, we present a generic approach for developing polynomial-time inference algorithms for a family of criteria and discuss the potential (dis)advantages of some commonly used criteria. Finally, we envision future work aimed at providing a comprehensive understanding of inference complexity and criteria selection.
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Notes
- 1.
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Acknowledgments
This work was funded/supported by the Junior Professor Chair in Trustworthy AI (Ref. ANR-R311CHD), and the US Office of Naval Research Global under Grant N62909-23-1-2058.
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Nguyen, VL., Hoang, XT., Huynh, VN. (2023). Inference Problem in Probabilistic Multi-label Classification. In: Honda, K., Le, B., Huynh, VN., Inuiguchi, M., Kohda, Y. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2023. Lecture Notes in Computer Science(), vol 14376. Springer, Cham. https://doi.org/10.1007/978-3-031-46781-3_1
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