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

arXiv: https://arxiv.org/abs/2104.14787 · HTML: https://mlsanity.com/paper/2104.14787/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 62 references that are in the map.
- First posted on arXiv 30 Apr 2021.

## Builds on (most cited first, 4 of 4)

- [DeepPINK: reproducible feature selection in deep neural networks](https://mlsanity.com/paper/1809.01185/) (2018)
- [A Bayesian Perspective on Training Speed and Model Selection](https://mlsanity.com/paper/2010.14499/) (2020)
- [RENT -- Repeated Elastic Net Technique for Feature Selection](https://mlsanity.com/paper/2009.12780/) (2020)
- [The Implicit Metropolis-Hastings Algorithm](https://mlsanity.com/paper/1906.03644/) (2019)

## Built on by (most cited first, 4 of 4)

- [AFS-BM: Enhancing Model Performance through Adaptive Feature Selection with Binary Masking](https://mlsanity.com/paper/2401.11250/) (2024)
- [Ranking Feature-Block Importance in Artificial Multiblock Neural Networks](https://mlsanity.com/paper/2109.10279/) (2021)
- [Towards Understanding the Survival of Patients with High-Grade Gastroenteropancreatic Neuroendocrine Neoplasms: An Investigation of Ensemble Feature Selection in the Prediction of Overall Survival](https://mlsanity.com/paper/2302.10106/) (2023)
- [Hierarchical Ensemble-Based Feature Selection for Time Series Forecasting](https://mlsanity.com/paper/2310.17544/) (2023)

## Abstract (arXiv)

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.