Synthetic Data for Feature Selection

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dc.contributor.authorKamalov, Firuz
dc.contributor.authorSulieman, Hana
dc.contributor.authorCherukuri, Aswani Kumar
dc.date.accessioned2023-12-26T05:30:19Z
dc.date.available2023-12-26T05:30:19Z
dc.date.copyright© 2023
dc.date.issued2023
dc.description.abstractFeature 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.
dc.identifier.citationKamalov, 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_32
dc.identifier.isbn978-303142507-3
dc.identifier.issn03029743
dc.identifier.urihttps://doi.org/10.1007/978-3-031-42508-0_32
dc.identifier.urihttps://hdl.handle.net/20.500.12519/966
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relationAuthors Affiliations : Kamalov, F., Department of Electrical Engineering, Canadian University Dubai, Dubai, United Arab Emirates; Sulieman, H., Department of Mathematics and Statistics, American University of Sharjah, Sharjah, United Arab Emirates; Cherukuri, A.K., School of IT and Engineering, Vellore Institute of Technology, Vellore, India
dc.relation.ispartofseriesInternational Conference on Artificial Intelligence and Soft Computing. Lecture Notes in Computer Science; Volume 14126
dc.rightsLicense to reuse abstract has been secured from Springer Nature and Copyright Clearance Center.
dc.rights.holderCopyright : © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
dc.subjectelectronics
dc.subjectfeature selection
dc.subjectsynthetic data
dc.titleSynthetic Data for Feature Selection
dc.typeConference Paper

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