  Feature selection is an important and active field of research in machine
learning and data science. Our goal in this paper is to propose a collection of
synthetic datasets that can be used as a common reference point for feature
selection algorithms. Synthetic datasets allow for precise evaluation of
selected features and control of the data parameters for comprehensive
assessment. The proposed datasets are based on applications from electronics in
order to mimic real life scenarios. To illustrate the utility of the proposed
data we employ one of the datasets to test several popular feature selection
algorithms. The datasets are made publicly available on GitHub and can be used
by researchers to evaluate feature selection algorithms.
