Kamalov, Firuz2020-02-092020-02-0920182018-07-02Kamalov, F. (2018). 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.2018.002389781538668047http://dx.doi.org/10.1109/ICMLA.2018.00238https://hdl.handle.net/20.500.12519/118Sensitivity 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.enPermission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc.Big dataFeature selectionSensitivity analysisTotal sensitivity indexComputational efficiencyFeature extractionMachine learningSensitivity indicesSensitivity analysisConference Paper2018 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.