Searching for Machine Learning Pipelines Using a Context-Free Grammar

Authors

  • Radu Marinescu IBM Research
  • Akihiro Kishimoto IBM Research
  • Parikshit Ram IBM Research
  • Ambrish Rawat IBM Research
  • Martin Wistuba IBM Research
  • Paulito P. Palmes IBM Research
  • Adi Botea Eaton

Keywords:

Applications

Abstract

AutoML automatically selects, composes and parameterizes machine learning algorithms into a workflow or pipeline of operations that aims at maximizing performance on a given dataset. Although current methods for AutoML achieved impressive results they mostly concentrate on optimizing fixed linear workflows. In this paper, we take a different approach and focus on generating and optimizing pipelines of complex directed acyclic graph shapes. These complex pipeline structure may lead to discovering hidden features and thus boost performance considerably. We explore the power of heuristic search and context-free grammars to search and optimize these kinds of pipelines. Experiments on various benchmark datasets show that our approach is highly competitive and often outperforms existing AutoML systems.

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Published

2021-05-18

How to Cite

Marinescu, R., Kishimoto, A., Ram, P., Rawat, A., Wistuba, M., Palmes, P. P., & Botea, A. (2021). Searching for Machine Learning Pipelines Using a Context-Free Grammar. Proceedings of the AAAI Conference on Artificial Intelligence, 35(10), 8902-8911. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17077

Issue

Section

AAAI Technical Track on Machine Learning III