Kamalov, FiruzElnaffar, SaidSulieman, HanaCherukuri, Aswani Kumar2023-08-112023-08-112023-03Kamalov, F., Elnaffar, S., Sulieman, H., & Cherukuri, A. K. (2023). XyGen: Synthetic data generator for feature selection. Software Impacts, 15, 100485. https://doi.org/10.1016/j.simpa.2023.10048526659638https://doi.org/10.1016/j.simpa.2023.100485https://hdl.handle.net/20.500.12519/812Given the large number of feature selection algorithms, it has become imperative to have a uniform procedure for evaluating the performance of the algorithms. We propose a library of synthetic datasets designed specifically to test the effectiveness of feature selection algorithms. The datasets are inspired by applications in the field of electronics and have a range of characteristics to provide a variety of test scenarios. The software comes in the form of a Python library with standard interface for loading and generating datasets. Each dataset is implemented as a function that allows control of various parameters of the data. © 2023 The Author(s)enThis is an open-access article under Creative Commons Attribution (CC BY 4.0)Data miningFeature selectionMachine learningSynthetic dataArticleCopyright : © 2023 The Author(s). Published by Elsevier B.V.