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Learning Preferences for Large Scale Multi-label Problems

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 11139)


Despite that the majority of machine learning approaches aim to solve binary classification problems, several real-world applications 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 effectiveness and efficiency in a real-world large-scale multi-label task.


  • Preference Learning Machine
  • Multi-class
  • Multi-label
  • Big data
  • Large-scale

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Correspondence to Ivano Lauriola .

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Lauriola, I., Polato, M., Lavelli, A., Rinaldi, F., Aiolli, F. (2018). Learning Preferences for Large Scale Multi-label Problems. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11139. Springer, Cham.

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