LCEN: A Novel Feature Selection Algorithm for Nonlinear, Interpretable
  Machine Learning Models
Pedro Seber Richard D. Braatz
Interpretable architectures can have advantages over black-box architectures,
and interpretability is essential for the application of machine learning in
critical settings, such as aviation or medicine. However, the simplest, most
commonly used interpretable architectures (such as LASSO or EN) are limited to
linear predictions and have poor feature selection capabilities. In this work,
we introduce the LASSO-Clip-EN (LCEN) algorithm for the creation of nonlinear,
interpretable machine learning models. LCEN is tested on a wide variety of
artificial and empirical datasets, creating more accurate, sparser models than
other commonly used architectures. These experiments reveal that LCEN is robust
against many issues typically present in datasets and modeling, including
noise, multicollinearity, data scarcity, and hyperparameter variance. LCEN is
also able to rediscover multiple physical laws from empirical data and, for
processes with no known physical laws, LCEN achieves better results than many
other dense and sparse methods -- including using 10.8 times fewer features
than dense methods and 8.1 times fewer features than EN on one dataset, and is
comparable to an ANN on another dataset.