  The amount of data for machine learning (ML) applications is constantly
growing. Not only the number of observations, especially the number of measured
variables (features) increases with ongoing digitization. Selecting the most
appropriate features for predictive modeling is an important lever for the
success of ML applications in business and research. Feature selection methods
(FSM) that are independent of a certain ML algorithm - so-called filter methods
- have been numerously suggested, but little guidance for researchers and
quantitative modelers exists to choose appropriate approaches for typical ML
problems. This review synthesizes the substantial literature on feature
selection benchmarking and evaluates the performance of 58 methods in the
widely used R environment. For concrete guidance, we consider four typical
dataset scenarios that are challenging for ML models (noisy, redundant,
imbalanced data and cases with more features than observations). Drawing on the
experience of earlier benchmarks, which have considered much fewer FSMs, we
compare the performance of the methods according to four criteria (predictive
performance, number of relevant features selected, stability of the feature
sets and runtime). We found methods relying on the random forest approach, the
double input symmetrical relevance filter (DISR) and the joint impurity filter
(JIM) were well-performing candidate methods for the given dataset scenarios.
