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Sensitivity analysis for feature selection

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dc.contributor.author Kamalov, Firuz
dc.date.accessioned 2020-02-09T10:01:31Z
dc.date.available 2020-02-09T10:01:31Z
dc.date.copyright 2018 en_US
dc.date.issued 2019
dc.identifier.citation Kamalov, F. (2019). Sensitivity Analysis for Feature Selection. In Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018 (pp. 1466–1470). https://doi.org/10.1109/ICMLA.2018.00238 en_US
dc.identifier.isbn 9781538668047
dc.identifier.uri http://dx.doi.org/10.1109/ICMLA.2018.00238
dc.identifier.uri https://hdl.handle.net/20.500.12519/118
dc.description This conference paper is not available at CUD collection. The version of scholarly record of this conference paper is published in 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) (2019), available online at: https://doi.org/10.1109/ICMLA.2018.00238. en_US
dc.description.abstract Sensitivity analysis allows us to decompose the variance output into its source components. Total sensitivity index represents the effects of varying a feature on the variance of the target variable. In this paper we use total sensitivity index to evaluate features for the purpose of feature selection. We test our method on various data sets and compare its performance relative to other modern feature selection methods. The proposed method produces very robust results with high computational efficiency. © 2018 IEEE. en_US
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation Author Affiliation: Kamalov, F., Department of Electrical Engineering, Canadian University Dubai, Dubai, United Arab Emirates
dc.relation.ispartofseries Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018;
dc.rights Permission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc.
dc.rights.uri https://www.ieee.org/publications/rights/rights-policies.html
dc.subject Big data en_US
dc.subject Feature selection en_US
dc.subject Sensitivity analysis en_US
dc.subject Total sensitivity index en_US
dc.subject Computational efficiency en_US
dc.subject Feature extraction en_US
dc.subject Machine learning en_US
dc.subject Sensitivity indices en_US
dc.subject Sensitivity analysis en_US
dc.title Sensitivity analysis for feature selection en_US
dc.type Conference Paper en_US
dc.rights.holder 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.


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