---
_id: '3852'
abstract:
- lang: eng
  text: "In automated machine learning (AutoML), the process of engineering machine
    learning applications with respect to a specific problem is (partially) automated.\r\nVarious
    AutoML tools have already been introduced to provide out-of-the-box machine learning
    functionality.\r\nMore specifically, by selecting machine learning algorithms
    and optimizing their hyperparameters, these tools produce a machine learning pipeline
    tailored to the problem at hand.\r\nExcept for TPOT, all of these tools restrict
    the maximum number of processing steps of such a pipeline.\r\nHowever, as TPOT
    follows an evolutionary approach, it suffers from performance issues when dealing
    with larger datasets.\r\nIn this paper, we present an alternative approach leveraging
    a hierarchical planning to configure machine learning pipelines that are unlimited
    in length.\r\nWe evaluate our approach and find its performance to be competitive
    with other AutoML tools, including TPOT."
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: Felix
  full_name: Mohr, Felix
  last_name: Mohr
- first_name: Eyke
  full_name: Hüllermeier, Eyke
  id: '48129'
  last_name: Hüllermeier
citation:
  ama: 'Wever MD, Mohr F, Hüllermeier E. ML-Plan for Unlimited-Length Machine Learning
    Pipelines. In: <i>ICML 2018 AutoML Workshop</i>. ; 2018.'
  apa: Wever, M. D., Mohr, F., &#38; Hüllermeier, E. (2018). ML-Plan for Unlimited-Length
    Machine Learning Pipelines. In <i>ICML 2018 AutoML Workshop</i>. Stockholm, Sweden.
  bibtex: '@inproceedings{Wever_Mohr_Hüllermeier_2018, title={ML-Plan for Unlimited-Length
    Machine Learning Pipelines}, booktitle={ICML 2018 AutoML Workshop}, author={Wever,
    Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2018} }'
  chicago: Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “ML-Plan for Unlimited-Length
    Machine Learning Pipelines.” In <i>ICML 2018 AutoML Workshop</i>, 2018.
  ieee: M. D. Wever, F. Mohr, and E. Hüllermeier, “ML-Plan for Unlimited-Length Machine
    Learning Pipelines,” in <i>ICML 2018 AutoML Workshop</i>, Stockholm, Sweden, 2018.
  mla: Wever, Marcel Dominik, et al. “ML-Plan for Unlimited-Length Machine Learning
    Pipelines.” <i>ICML 2018 AutoML Workshop</i>, 2018.
  short: 'M.D. Wever, F. Mohr, E. Hüllermeier, in: ICML 2018 AutoML Workshop, 2018.'
conference:
  end_date: 2018-07-15
  location: Stockholm, Sweden
  name: ICML 2018 AutoML Workshop
  start_date: 2018-07-10
date_created: 2018-08-09T06:14:54Z
date_updated: 2022-01-06T06:59:46Z
ddc:
- '006'
department:
- _id: '355'
file:
- access_level: open_access
  content_type: application/pdf
  creator: wever
  date_created: 2018-08-09T06:14:43Z
  date_updated: 2018-08-09T06:14:43Z
  file_id: '3853'
  file_name: 38.pdf
  file_size: 297811
  relation: main_file
file_date_updated: 2018-08-09T06:14:43Z
has_accepted_license: '1'
keyword:
- automated machine learning
- complex pipelines
- hierarchical planning
language:
- iso: eng
main_file_link:
- url: https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxhdXRvbWwyMDE4aWNtbHxneDo3M2Q3MjUzYjViNDRhZTAx
oa: '1'
project:
- _id: '1'
  name: SFB 901
- _id: '3'
  name: SFB 901 - Project Area B
- _id: '10'
  name: SFB 901 - Subproject B2
publication: ICML 2018 AutoML Workshop
quality_controlled: '1'
status: public
title: ML-Plan for Unlimited-Length Machine Learning Pipelines
type: conference
urn: '38527'
user_id: '49109'
year: '2018'
...