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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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A rapid feature selection method for catalyst design: Iterative Bayesian additive regression trees (iBART).

Chun-Yen Liu1, Shengbin Ye2, Meng Li2

  • 1Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas 77005, USA.

The Journal of Chemical Physics
|April 30, 2022
PubMed
Summary
This summary is machine-generated.

A new iterative method (iBART) combines feature engineering and selection to efficiently create predictive descriptors for materials science. This approach significantly reduces computational cost while maintaining high performance in predicting system properties.

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Area of Science:

  • Computational Materials Science
  • Machine Learning for Chemistry and Physics
  • Data-driven Scientific Discovery

Background:

  • Feature selection (FS) and feature engineering (FE) are crucial for developing predictive models in materials science.
  • Traditional methods struggle with large, correlated feature spaces generated by recursive FE, leading to high computational demands.
  • Existing FS methods face challenges with collinearity and computational complexity in large feature spaces.

Purpose of the Study:

  • To develop a computationally efficient method for generating predictive descriptors by interleaving feature engineering and selection.
  • To address the limitations of traditional FS approaches in handling highly correlated and massive feature spaces.
  • To introduce iterative Bayesian additive regression trees (iBART) for progressive descriptor building and selection.

Main Methods:

  • Developed iterative Bayesian additive regression trees (iBART), a novel method that interleaves feature engineering (FE) and feature selection (FS).
  • iBART utilizes Bayesian additive regression trees (BART) for FS and FE with unary/binary operators in an iterative process.
  • Compared iBART's performance against state-of-the-art FS methods (LASSO+l0, SIS, Bayesian FS) using catalysis metal-support interactions as a test case.

Main Results:

  • iBART achieved performance comparable to existing state-of-the-art FS methods.
  • The iBART method significantly reduced computational resources compared to traditional one-shot FE/FS approaches.
  • iBART generated a maximum feature space of O(10^2), drastically smaller than the O(10^6) from one-shot methods.

Conclusions:

  • The iBART method offers a computationally efficient alternative for developing data-driven descriptors in materials science.
  • Interleaving FE and FS steps is an effective strategy to manage large feature spaces and reduce computational load.
  • iBART demonstrates strong potential for accelerating the prediction of physical and chemical system properties.