---
_id: '3510'
abstract:
- lang: eng
  text: Automated machine learning (AutoML) seeks to automatically select, compose,
    and parametrize machine learning algorithms, so as to achieve optimal performance
    on a given task (dataset). Although current approaches to AutoML have already
    produced impressive results, the field is still far from mature, and new techniques
    are still being developed. In this paper, we present ML-Plan, a new approach to
    AutoML based on hierarchical planning. To highlight the potential of this approach,
    we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn,
    and TPOT. In an extensive series of experiments, we show that ML-Plan is highly
    competitive and often outperforms existing approaches.
article_type: original
author:
- first_name: Felix
  full_name: Mohr, Felix
  last_name: Mohr
- first_name: Marcel Dominik
  full_name: Wever, Marcel Dominik
  id: '33176'
  last_name: Wever
  orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
  full_name: Hüllermeier, Eyke
  id: '48129'
  last_name: Hüllermeier
citation:
  ama: 'Mohr F, Wever MD, Hüllermeier E. ML-Plan: Automated Machine Learning via Hierarchical
    Planning. <i>Machine Learning</i>. Published online 2018:1495-1515. doi:<a href="https://doi.org/10.1007/s10994-018-5735-z">10.1007/s10994-018-5735-z</a>'
  apa: 'Mohr, F., Wever, M. D., &#38; Hüllermeier, E. (2018). ML-Plan: Automated Machine
    Learning via Hierarchical Planning. <i>Machine Learning</i>, 1495–1515. <a href="https://doi.org/10.1007/s10994-018-5735-z">https://doi.org/10.1007/s10994-018-5735-z</a>'
  bibtex: '@article{Mohr_Wever_Hüllermeier_2018, title={ML-Plan: Automated Machine
    Learning via Hierarchical Planning}, DOI={<a href="https://doi.org/10.1007/s10994-018-5735-z">10.1007/s10994-018-5735-z</a>},
    journal={Machine Learning}, publisher={Springer}, author={Mohr, Felix and Wever,
    Marcel Dominik and Hüllermeier, Eyke}, year={2018}, pages={1495–1515} }'
  chicago: 'Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “ML-Plan: Automated
    Machine Learning via Hierarchical Planning.” <i>Machine Learning</i>, 2018, 1495–1515.
    <a href="https://doi.org/10.1007/s10994-018-5735-z">https://doi.org/10.1007/s10994-018-5735-z</a>.'
  ieee: 'F. Mohr, M. D. Wever, and E. Hüllermeier, “ML-Plan: Automated Machine Learning
    via Hierarchical Planning,” <i>Machine Learning</i>, pp. 1495–1515, 2018, doi:
    <a href="https://doi.org/10.1007/s10994-018-5735-z">10.1007/s10994-018-5735-z</a>.'
  mla: 'Mohr, Felix, et al. “ML-Plan: Automated Machine Learning via Hierarchical
    Planning.” <i>Machine Learning</i>, Springer, 2018, pp. 1495–515, doi:<a href="https://doi.org/10.1007/s10994-018-5735-z">10.1007/s10994-018-5735-z</a>.'
  short: F. Mohr, M.D. Wever, E. Hüllermeier, Machine Learning (2018) 1495–1515.
conference:
  end_date: 2018-09-14
  location: Dublin, Ireland
  name: European Conference on Machine Learning and Principles and Practice of Knowledge
    Discovery in Databases
  start_date: 2018-09-10
date_created: 2018-07-08T14:06:14Z
date_updated: 2022-01-06T06:59:21Z
ddc:
- '000'
department:
- _id: '355'
- _id: '34'
- _id: '7'
- _id: '26'
doi: 10.1007/s10994-018-5735-z
file:
- access_level: closed
  content_type: application/pdf
  creator: ups
  date_created: 2018-11-02T15:32:16Z
  date_updated: 2018-11-02T15:32:16Z
  file_id: '5306'
  file_name: ML-PlanAutomatedMachineLearnin.pdf
  file_size: 1070937
  relation: main_file
  success: 1
file_date_updated: 2018-11-02T15:32:16Z
has_accepted_license: '1'
keyword:
- AutoML
- Hierarchical Planning
- HTN planning
- ML-Plan
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://rdcu.be/3Nc2
oa: '1'
page: 1495-1515
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: Machine Learning
publication_identifier:
  eissn:
  - 1573-0565
  issn:
  - 0885-6125
publication_status: epub_ahead
publisher: Springer
status: public
title: 'ML-Plan: Automated Machine Learning via Hierarchical Planning'
type: journal_article
user_id: '5786'
year: '2018'
...