Finite Sample Based Mutual Information

dc.contributor.author Rajab, Khairan
dc.contributor.author Kamalov, Firuz
dc.date.accessioned 2021-09-12T11:42:04Z
dc.date.available 2021-09-12T11:42:04Z
dc.date.copyright © 2013
dc.date.issued 2021
dc.description This article is not available at CUD collection. The version of scholarly record of this article is published in IEEE Access (2021), available online at: https://doi.org/10.1109/ACCESS.2021.3107031 en_US
dc.description.abstract Mutual information is a popular metric in machine learning. In case of a discrete target variable and a continuous feature variable the mutual information can be calculated as a sum-integral of weighted log likelihood ratio of joint and marginal density distributions. However, in practice the true density distributions are unavailable and only a finite sample of the population is given. In this paper, we propose a novel method for calculating the mutual information for continuous variables using a finite sample of the population. The proposed method is based on approximating the underlying continuous density distribution using Kernel Density Estimation. Unlike previous kernel-based approaches for estimating mutual information, our method calculates directly the integral involved in the formula. Numerical experiments demonstrate that the proposed method produces more accurate results than the currently used feature selection approaches. In addition, our method demonstrates substantially faster computation times than the benchmark methods. © 2013 IEEE. en_US
dc.description.sponsorship Najran University / NU/-/SERC/10/631 en_US
dc.identifier.citation Rajab, K., & Kamalov, F. (2021). Finite sample based mutual information. IEEE Access, 9, 118871-118879. https://doi.org/10.1109/ACCESS.2021.3107031 en_US
dc.identifier.issn 21693536
dc.identifier.uri https://doi.org/10.1109/ACCESS.2021.3107031
dc.identifier.uri http://hdl.handle.net/20.500.12519/439
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation Authors Affiliations : Kamalov, F., Department of Electrical Engineering, Canadian University of Dubai, Dubai, UAE. (e-mail: firuz@cud.ac.ae); Rajab, K., College of Computer Science and Information System, Najran University, Najran, KSA.
dc.relation.ispartofseries IEEE Access;Volume 9
dc.rights Creative Commons Attribution 4.0 International (CC BY 4.0) License
dc.rights.holder Copyright : © 2013 IEEE.
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject Continuous variable en_US
dc.subject feature evaluation en_US
dc.subject feature selection en_US
dc.subject finite sample en_US
dc.subject kernel density estimation en_US
dc.subject mutual information en_US
dc.title Finite Sample Based Mutual Information en_US
dc.type Article en_US
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