Abstract
This paper proposes an approach for multi-label classification based on metric learning. The approach has been designed to deal with general classification problems, without any assumption on the specific kind of data used (images, text, etc.) or semantic meaning assigned to labels (tags, categories, etc.). It is based on clustering and metric learning algorithm aimed at constructing a space capable of facilitating and improving the task of classifiers. The experimental results obtained on public benchmarks of different nature confirm the effectiveness of the proposal.
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Brighi, M., Franco, A., Maio, D. (2021). Metric Learning for Multi-label Classification. In: Torsello, A., Rossi, L., Pelillo, M., Biggio, B., Robles-Kelly, A. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2021. Lecture Notes in Computer Science(), vol 12644. Springer, Cham. https://doi.org/10.1007/978-3-030-73973-7_3
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DOI: https://doi.org/10.1007/978-3-030-73973-7_3
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