Sensitivity analysis for feature selection

dc.contributor.authorKamalov, Firuz
dc.descriptionThis 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:
dc.description.abstractSensitivity 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.identifier.citationKamalov, F. (2019). Sensitivity Analysis for Feature Selection. In Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018 (pp. 1466–1470).
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relationAuthor Affiliation: Kamalov, F., Department of Electrical Engineering, Canadian University Dubai, Dubai, United Arab Emirates
dc.relation.ispartofseriesProceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018;
dc.rightsPermission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc.
dc.rights.holder2018 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.
dc.subjectBig dataen_US
dc.subjectFeature selectionen_US
dc.subjectSensitivity analysisen_US
dc.subjectTotal sensitivity indexen_US
dc.subjectComputational efficiencyen_US
dc.subjectFeature extractionen_US
dc.subjectMachine learningen_US
dc.subjectSensitivity indicesen_US
dc.subjectSensitivity analysisen_US
dc.titleSensitivity analysis for feature selectionen_US
dc.typeConference Paperen_US
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