LiBRe

Label-Wise Selection of Base Learners in Binary Relevance for Multi-label Classification

verfasst von

Marcel Wever, Alexander Tornede, Felix Mohr, Eyke Hüllermeier

Abstract

In multi-label classification (MLC), each instance is associated with a set of class labels, in contrast to standard classification, where an instance is assigned a single label. Binary relevance (BR) learning, which reduces a multi-label to a set of binary classification problems, one per label, is arguably the most straight-forward approach to MLC. In spite of its simplicity, BR proved to be competitive to more sophisticated MLC methods, and still achieves state-of-the-art performance for many loss functions. Somewhat surprisingly, the optimal choice of the base learner for tackling the binary classification problems has received very little attention so far. Taking advantage of the label independence assumption inherent to BR, we propose a label-wise base learner selection method optimizing label-wise macro averaged performance measures. In an extensive experimental evaluation, we find that or approach, called LiBRe, can significantly improve generalization performance.

Externe Organisation(en)

Heinz Nixdorf Institut (HNI)
Universität Paderborn
Universidad de la Sabana

Typ

Aufsatz in Konferenzband

Seiten

561-573

Anzahl der Seiten

13

Publikationsdatum

2020

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Theoretische Informatik, Allgemeine Computerwissenschaft

Elektronische Version(en)

https://link.springer.com/chapter/10.1007/978-3-030-44584-3_44 (Zugang: Offen)
https://doi.org/10.1007/978-3-030-44584-3_44 (Zugang: Offen)