Automatic Feature Engineering Through Monte Carlo Tree Search

Research output: Contribution to book/Conference proceedings/Anthology/ReportConference contributionContributedpeer-review

Contributors

  • Yiran Huang - , Karlsruhe Institute of Technology (Author)
  • Yexu Zhou - , Karlsruhe Institute of Technology (Author)
  • Michael Hefenbrock - , Karlsruhe Institute of Technology (Author)
  • Till Riedel - , Karlsruhe Institute of Technology (Author)
  • Likun Fang - , Karlsruhe Institute of Technology (Author)
  • Michael Beigl - , Karlsruhe Institute of Technology (Author)

Abstract

The performance of machine learning models depends heavily on the feature space and feature engineering. Although neural networks have made significant progress in learning latent feature spaces from data, compositional feature engineering through nested feature transformations can reduce model complexity and can be particularly desirable for interpretability. To find suitable transformations automatically, state-of-the-art methods model the feature transformation space by graph structures and use heuristics such as ϵ -greedy to search for them. Such search strategies tend to become less efficient over time because they do not consider the sequential information of the candidate sequences and cannot dynamically adjust the heuristic strategy. To address these shortcomings, we propose a reinforcement learning-based automatic feature engineering method, which we call Monte Carlo tree search Automatic Feature Engineering (mCAFE). We employ a surrogate model that can capture the sequential information contained in the transformation sequence and thus can dynamically adjust the exploration strategy. It balances exploration and exploitation by Thompson sampling and uses a Long Short Term Memory (LSTM) based surrogate model to estimate sequences of promising transformations. In our experiments, mCAFE outperformed state-of-the-art automatic feature engineering methods on most common benchmark datasets.

Details

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases
EditorsMassih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas
PublisherSpringer, Cham
Pages581–598
Number of pages18
ISBN (electronic)978-3-031-26409-2
ISBN (print)978-3-031-26408-5
Publication statusPublished - 2023
Peer-reviewedYes
Externally publishedYes

Publication series

SeriesLecture Notes in Computer Science, Volume 13715
ISSN0302-9743

External IDs

Scopus 85151051582

Keywords

Keywords

  • Data mining, Feature engineering, Monte Carlo tree search, Reinforce learning