---
_id: '15002'
abstract:
- lang: eng
  text: Many problem settings in machine learning are concerned with the simultaneous
    prediction of multiple target variables of diverse type. Amongst others, such
    problem settings arise in multivariate regression, multi-label classification,
    multi-task learning, dyadic prediction, zero-shot learning, network inference,
    and matrix completion. These subfields of machine learning are typically studied
    in isolation, without highlighting or exploring important relationships. In this
    paper, we present a unifying view on what we call multi-target prediction (MTP)
    problems and methods. First, we formally discuss commonalities and differences
    between existing MTP problems. To this end, we introduce a general framework that
    covers the above subfields as special cases. As a second contribution, we provide
    a structured overview of MTP methods. This is accomplished by identifying a number
    of key properties, which distinguish such methods and determine their suitability
    for different types of problems. Finally, we also discuss a few challenges for
    future research.
author:
- first_name: Willem
  full_name: Waegeman, Willem
  last_name: Waegeman
- first_name: Krzysztof
  full_name: Dembczynski, Krzysztof
  last_name: Dembczynski
- first_name: Eyke
  full_name: Hüllermeier, Eyke
  id: '48129'
  last_name: Hüllermeier
citation:
  ama: 'Waegeman W, Dembczynski K, Hüllermeier E. Multi-target prediction: a unifying
    view on problems and methods. <i>Data Mining and Knowledge Discovery</i>. 2019;33(2):293-324.
    doi:<a href="https://doi.org/10.1007/s10618-018-0595-5">10.1007/s10618-018-0595-5</a>'
  apa: 'Waegeman, W., Dembczynski, K., &#38; Hüllermeier, E. (2019). Multi-target
    prediction: a unifying view on problems and methods. <i>Data Mining and Knowledge
    Discovery</i>, <i>33</i>(2), 293–324. <a href="https://doi.org/10.1007/s10618-018-0595-5">https://doi.org/10.1007/s10618-018-0595-5</a>'
  bibtex: '@article{Waegeman_Dembczynski_Hüllermeier_2019, title={Multi-target prediction:
    a unifying view on problems and methods}, volume={33}, DOI={<a href="https://doi.org/10.1007/s10618-018-0595-5">10.1007/s10618-018-0595-5</a>},
    number={2}, journal={Data Mining and Knowledge Discovery}, author={Waegeman, Willem
    and Dembczynski, Krzysztof and Hüllermeier, Eyke}, year={2019}, pages={293–324}
    }'
  chicago: 'Waegeman, Willem, Krzysztof Dembczynski, and Eyke Hüllermeier. “Multi-Target
    Prediction: A Unifying View on Problems and Methods.” <i>Data Mining and Knowledge
    Discovery</i> 33, no. 2 (2019): 293–324. <a href="https://doi.org/10.1007/s10618-018-0595-5">https://doi.org/10.1007/s10618-018-0595-5</a>.'
  ieee: 'W. Waegeman, K. Dembczynski, and E. Hüllermeier, “Multi-target prediction:
    a unifying view on problems and methods,” <i>Data Mining and Knowledge Discovery</i>,
    vol. 33, no. 2, pp. 293–324, 2019.'
  mla: 'Waegeman, Willem, et al. “Multi-Target Prediction: A Unifying View on Problems
    and Methods.” <i>Data Mining and Knowledge Discovery</i>, vol. 33, no. 2, 2019,
    pp. 293–324, doi:<a href="https://doi.org/10.1007/s10618-018-0595-5">10.1007/s10618-018-0595-5</a>.'
  short: W. Waegeman, K. Dembczynski, E. Hüllermeier, Data Mining and Knowledge Discovery
    33 (2019) 293–324.
date_created: 2019-11-15T10:16:34Z
date_updated: 2022-01-06T06:52:14Z
ddc:
- '000'
department:
- _id: '34'
- _id: '355'
doi: 10.1007/s10618-018-0595-5
file:
- access_level: open_access
  content_type: application/pdf
  creator: lettmann
  date_created: 2020-02-28T12:43:39Z
  date_updated: 2020-02-28T12:45:26Z
  file_id: '16155'
  file_name: multi-target-prediction.pdf
  file_size: 837808
  relation: main_file
file_date_updated: 2020-02-28T12:45:26Z
has_accepted_license: '1'
intvolume: '        33'
issue: '2'
language:
- iso: eng
oa: '1'
page: 293-324
publication: Data Mining and Knowledge Discovery
publication_identifier:
  issn:
  - 1573-756X
status: public
title: 'Multi-target prediction: a unifying view on problems and methods'
type: journal_article
user_id: '315'
volume: 33
year: '2019'
...