A User-Guided Bayesian Framework for Ensemble Feature Selection in Life Science Applications (UBayFS)

TL;DR

UBayFS is a Bayesian ensemble feature selection method that incorporates user domain knowledge and data to improve interpretability and performance in high-dimensional life science datasets.

Contribution

It introduces a novel Bayesian framework that integrates user guidance with ensemble feature selection, enhancing interpretability and balancing data-driven and knowledge-driven insights.

Findings

Balances user knowledge and data observations effectively

Achieves competitive performance with state-of-the-art methods

Enhances interpretability in high-dimensional datasets

Abstract

Feature selection represents a measure to reduce the complexity of high-dimensional datasets and gain insights into the systematic variation in the data. This aspect is of specific importance in domains that rely on model interpretability, such as life sciences. We propose UBayFS, an ensemble feature selection technique embedded in a Bayesian statistical framework. Our approach considers two sources of information: data and domain knowledge. We build a meta-model from an ensemble of elementary feature selectors and aggregate this information in a multinomial likelihood. The user guides UBayFS by weighting features and penalizing specific feature blocks or combinations, implemented via a Dirichlet-type prior distribution and a regularization term. In a quantitative evaluation, we demonstrate that our framework (a) allows for a balanced trade-off between user knowledge and data observations, and (b) achieves competitive performance with state-of-the-art methods.