Generalized feature similarity measure

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dc.contributor.authorKamalov, Firuz
dc.date.accessioned2020-06-15T10:00:09Z
dc.date.available2020-06-15T10:00:09Z
dc.date.copyright2020
dc.date.issued2020
dc.descriptionThis article is not available at CUD collection. The version of scholarly record of this article is published in Annals of Mathematics and Artificial Intelligence (2020), available online at: https://doi.org/10.1007/s10472-020-09700-8en_US
dc.description.abstractQuantifying the degree of relation between a feature and target class is one of the key aspects of machine learning. In this regard, information gain (IG) and χ2 are two of the most widely used measures in feature evaluation. In this paper, we discuss a novel approach to unifying these and other existing feature evaluation measures under a common framework. In particular, we introduce a new generalized family of measures to estimate the similarity between features. We show that the proposed set of measures satisfies all the general criteria for quantifying the relationship between features. We demonstrate that IG and χ2 are special cases of the generalized measure. We also analyze some of the topological and set-theoretic aspects of the family of functions that satisfy the criteria of our generalized measure. Finally, we produce novel feature evaluation measures using our approach and analyze their performance through numerical experiments. We show that a diverse array of measures can be created under our framework which can be used in applications such fusion based feature selection. © 2020, Springer Nature Switzerland AG.en_US
dc.identifier.citationKamalov, F. (2020). Generalized feature similarity measure. Annals of Mathematics and Artificial Intelligence. https://doi.org/10.1007/s10472-020-09700-8en_US
dc.identifier.issn10122443
dc.identifier.urihttps://doi.org/10.1007/s10472-020-09700-8
dc.identifier.urihttp://hdl.handle.net/20.500.12519/218
dc.language.isoen_USen_US
dc.publisherSpringeren_US
dc.relationAuthor Affiliation : Kamalov, F., Department of Electrical Engineering, Canadian University Dubai, Dubai, United Arab Emirates
dc.relation.ispartofseriesAnnals of Mathematics and Artificial Intelligence;
dc.rightsLicense to reuse the abstract has been secured from Springer Nature and Copyright Clearance Center.
dc.rights.holderCopyright : © 2020, Springer Nature Switzerland AG.
dc.rights.urihttps://s100.copyright.com/CustomerAdmin/PLF.jsp?ref=0959692d-f476-468a-a2bb-0dcad43c5604
dc.subjectFeature evaluation measuresen_US
dc.subjectFeature selectionen_US
dc.subjectInformation gainen_US
dc.subjectUnified frameworken_US
dc.subjectχ2en_US
dc.titleGeneralized feature similarity measureen_US
dc.typeArticleen_US
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