Kamalov, FiruzSulieman, HanaCherukuri, Aswani Kumar2023-12-262023-12-26© 20232023Kamalov, F., Sulieman, H., & Cherukuri, A. K. (2023, June). Synthetic data for feature selection. In International Conference on Artificial Intelligence and Soft Computing. Lecture Notes in Computer Science, 14126, (pp. 353-365). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-42508-0_32978-303142507-303029743https://doi.org/10.1007/978-3-031-42508-0_32https://hdl.handle.net/20.500.12519/966Feature 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. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.License to reuse abstract has been secured from Springer Nature and Copyright Clearance Center.electronicsfeature selectionsynthetic dataConference PaperCopyright : © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.