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Learning Preferences for Large Scale Multi-label Problems


Lauriola, Ivano; Polato, Mirko; Lavelli, Alberto; Rinaldi, Fabio; Aiolli, Fabio (2018). Learning Preferences for Large Scale Multi-label Problems. In: International Conference on Artificial Neural Networks, Rhodes, Greece, 4 October 2018 - 7 October 2018, 546-555.

Abstract

Despite that the majority of machine learning approaches
aim to solve binary classification problems, several real-world applica-
tions require specialized algorithms able to handle many different classes,
as in the case of single-label multi-class and multi-label classification
problems. The Label Ranking framework is a generalization of the above
mentioned settings, which aims to map instances from the input space
to a total order over the set of possible labels. However, generally these
algorithms are more complex than binary ones, and their application on
large-scale datasets could be untractable.
The main contribution of this work is the proposal of a novel general on-
line preference-based label ranking framework. The proposed framework
is able to solve binary, multi-class, multi-label and ranking problems. A
comparison with other baselines has been performed, showing effective-
ness and efficiency in a real-world large-scale multi-label task.

Abstract

Despite that the majority of machine learning approaches
aim to solve binary classification problems, several real-world applica-
tions require specialized algorithms able to handle many different classes,
as in the case of single-label multi-class and multi-label classification
problems. The Label Ranking framework is a generalization of the above
mentioned settings, which aims to map instances from the input space
to a total order over the set of possible labels. However, generally these
algorithms are more complex than binary ones, and their application on
large-scale datasets could be untractable.
The main contribution of this work is the proposal of a novel general on-
line preference-based label ranking framework. The proposed framework
is able to solve binary, multi-class, multi-label and ranking problems. A
comparison with other baselines has been performed, showing effective-
ness and efficiency in a real-world large-scale multi-label task.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Computational Linguistics
Dewey Decimal Classification:000 Computer science, knowledge & systems
410 Linguistics
Event End Date:7 October 2018
Deposited On:07 Mar 2019 10:46
Last Modified:10 Oct 2019 00:00
Publisher:Springer
Series Name:Lecture Notes in Computer Science
ISSN:0302-9743
ISBN:9783030014179
OA Status:Green
Publisher DOI:https://doi.org/10.1007/978-3-030-01418-6_54

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