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.

Metric Learning for Multi-label Classification / Marco Brighi, Annalisa Franco, Dario Maio. - ELETTRONICO. - (2021), pp. 24-33. (Intervento presentato al convegno Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) tenutosi a Padova nel 21/01/2022 - 22/01/2022).

Metric Learning for Multi-label Classification

Marco Brighi
Primo
Software
;
Annalisa Franco
Secondo
Methodology
;
Dario Maio
Ultimo
Validation
2021

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.
2021
Structural, Syntactic, and Statistical Pattern Recognition
24
33
Metric Learning for Multi-label Classification / Marco Brighi, Annalisa Franco, Dario Maio. - ELETTRONICO. - (2021), pp. 24-33. (Intervento presentato al convegno Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) tenutosi a Padova nel 21/01/2022 - 22/01/2022).
Marco Brighi, Annalisa Franco, Dario Maio
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/865927
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