AutoML for Multi-Label Classification: Overview and Empirical Evaluation

verfasst von

Marcel Wever, Alexander Tornede, Felix Mohr, Eyke Hüllermeier

Abstract

Automated machine learning (AutoML) supports the algorithmic construction and data-specific customization of machine learning pipelines, including the selection, combination, and parametrization of machine learning algorithms as main constituents. Generally speaking, AutoML approaches comprise two major components: a search space model and an optimizer for traversing the space. Recent approaches have shown impressive results in the realm of supervised learning, most notably (single-label) classification (SLC). Moreover, first attempts at extending these approaches towards multi-label classification (MLC) have been made. While the space of candidate pipelines is already huge in SLC, the complexity of the search space is raised to an even higher power in MLC. One may wonder, therefore, whether and to what extent optimizers established for SLC can scale to this increased complexity, and how they compare to each other. This paper makes the following contributions: First, we survey existing approaches to AutoML for MLC. Second, we augment these approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking framework that supports a fair and systematic comparison. Fourth, we conduct an extensive experimental study, evaluating the methods on a suite of MLC problems. We find a grammar-based best-first search to compare favorably to other optimizers.

Externe Organisation(en)

Universität Paderborn

Typ

Artikel

Journal

IEEE Transactions on Pattern Analysis and Machine Intelligence

Band

43

Seiten

3037-3054

Anzahl der Seiten

18

ISSN

0162-8828

Publikationsdatum

13.01.2021

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Software, Maschinelles Sehen und Mustererkennung, Theoretische Informatik und Mathematik, Artificial intelligence, Angewandte Mathematik

Elektronische Version(en)

https://doi.org/10.1109/TPAMI.2021.3051276 (Zugang: Offen)