MetaTPOT: Enhancing A Tree-based Pipeline Optimization Tool Using Meta-Learning

Doron Laadan, Roman Vainshtein, Yarden Curiel, Gilad Katz, Lior Rokach

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Automatic machine learning (AutoML) aims to automate the different aspects of the data science process and, by extension, allow non-experts to utilize "off the shelf" machine learning solution. One of the more popular AutoML methods is the Tree-based Pipeline Optimization Tool (TPOT), which uses genetic programming (GP) to efficiently explore the vast space of ML pipelines and produce a working ML solution. However, TPOT's GP process comes with substantial time and computational costs. In this study, we explore TPOT's GP process and propose MetaTPOT, an enhanced variant that uses a meta learning-based approach to predict the performance of TPOT's pipeline candidates. MetaTPOT leverages domain knowledge in the form of pipelines pre-ranking to improve TPOT's speed and performance. Evaluation on 65 classification datasets shows that our approach often improves the outcome of the genetic process while simultaneously substantially reduce its running time and computational cost.

Original languageEnglish
Title of host publicationCIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages2097-2100
Number of pages4
ISBN (Electronic)9781450368599
DOIs
StatePublished - 19 Oct 2020
Event29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - Virtual, Online, Ireland
Duration: 19 Oct 202023 Oct 2020

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference29th ACM International Conference on Information and Knowledge Management, CIKM 2020
Country/TerritoryIreland
CityVirtual, Online
Period19/10/2023/10/20

Keywords

  • automl
  • genetic programming(gp)
  • meta-learning
  • tpot

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