---
_id: '21004'
abstract:
- lang: eng
  text: '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.'
author:
- first_name: Marcel Dominik
  full_name: Wever, Marcel Dominik
  id: '33176'
  last_name: Wever
  orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Alexander
  full_name: Tornede, Alexander
  id: '38209'
  last_name: Tornede
- first_name: Felix
  full_name: Mohr, Felix
  last_name: Mohr
- first_name: Eyke
  full_name: Hüllermeier, Eyke
  id: '48129'
  last_name: Hüllermeier
citation:
  ama: 'Wever MD, Tornede A, Mohr F, Hüllermeier E. AutoML for Multi-Label Classification:
    Overview and Empirical Evaluation. <i>IEEE Transactions on Pattern Analysis and
    Machine Intelligence</i>. Published online 2021:1-1. doi:<a href="https://doi.org/10.1109/tpami.2021.3051276">10.1109/tpami.2021.3051276</a>'
  apa: 'Wever, M. D., Tornede, A., Mohr, F., &#38; Hüllermeier, E. (2021). AutoML
    for Multi-Label Classification: Overview and Empirical Evaluation. <i>IEEE Transactions
    on Pattern Analysis and Machine Intelligence</i>, 1–1. <a href="https://doi.org/10.1109/tpami.2021.3051276">https://doi.org/10.1109/tpami.2021.3051276</a>'
  bibtex: '@article{Wever_Tornede_Mohr_Hüllermeier_2021, title={AutoML for Multi-Label
    Classification: Overview and Empirical Evaluation}, DOI={<a href="https://doi.org/10.1109/tpami.2021.3051276">10.1109/tpami.2021.3051276</a>},
    journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, author={Wever,
    Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke},
    year={2021}, pages={1–1} }'
  chicago: 'Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier.
    “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” <i>IEEE
    Transactions on Pattern Analysis and Machine Intelligence</i>, 2021, 1–1. <a href="https://doi.org/10.1109/tpami.2021.3051276">https://doi.org/10.1109/tpami.2021.3051276</a>.'
  ieee: 'M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label
    Classification: Overview and Empirical Evaluation,” <i>IEEE Transactions on Pattern
    Analysis and Machine Intelligence</i>, pp. 1–1, 2021, doi: <a href="https://doi.org/10.1109/tpami.2021.3051276">10.1109/tpami.2021.3051276</a>.'
  mla: 'Wever, Marcel Dominik, et al. “AutoML for Multi-Label Classification: Overview
    and Empirical Evaluation.” <i>IEEE Transactions on Pattern Analysis and Machine
    Intelligence</i>, 2021, pp. 1–1, doi:<a href="https://doi.org/10.1109/tpami.2021.3051276">10.1109/tpami.2021.3051276</a>.'
  short: M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, IEEE Transactions on Pattern
    Analysis and Machine Intelligence (2021) 1–1.
date_created: 2021-01-16T14:48:13Z
date_updated: 2022-01-06T06:54:42Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
doi: 10.1109/tpami.2021.3051276
keyword:
- Automated Machine Learning
- Multi Label Classification
- Hierarchical Planning
- Bayesian Optimization
language:
- iso: eng
page: 1-1
project:
- _id: '1'
  name: SFB 901
- _id: '3'
  name: SFB 901 - Project Area B
- _id: '10'
  name: SFB 901 - Subproject B2
- _id: '52'
  name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_identifier:
  issn:
  - 0162-8828
  - 2160-9292
  - 1939-3539
publication_status: published
status: public
title: 'AutoML for Multi-Label Classification: Overview and Empirical Evaluation'
type: journal_article
user_id: '5786'
year: '2021'
...