  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.
