Generalized feature similarity measure

dc.contributor.author Kamalov, Firuz
dc.date.accessioned 2020-06-15T10:00:09Z
dc.date.available 2020-06-15T10:00:09Z
dc.date.copyright 2020
dc.date.issued 2020
dc.description This 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/s10472-020-09700-8 en_US
dc.description.abstract Quantifying 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.citation Kamalov, F. (2020). Generalized feature similarity measure. Annals of Mathematics and Artificial Intelligence. https://doi.org/10.1007/s10472-020-09700-8 en_US
dc.identifier.issn 10122443
dc.identifier.uri https://doi.org/10.1007/s10472-020-09700-8
dc.identifier.uri http://hdl.handle.net/20.500.12519/218
dc.language.iso en_US en_US
dc.publisher Springer en_US
dc.relation Author Affiliation : Kamalov, F., Department of Electrical Engineering, Canadian University Dubai, Dubai, United Arab Emirates
dc.relation.ispartofseries Annals of Mathematics and Artificial Intelligence;
dc.rights License to reuse the abstract has been secured from Springer Nature and Copyright Clearance Center.
dc.rights.holder Copyright : © 2020, Springer Nature Switzerland AG.
dc.rights.uri https://s100.copyright.com/CustomerAdmin/PLF.jsp?ref=0959692d-f476-468a-a2bb-0dcad43c5604
dc.subject Feature evaluation measures en_US
dc.subject Feature selection en_US
dc.subject Information gain en_US
dc.subject Unified framework en_US
dc.subject χ2 en_US
dc.title Generalized feature similarity measure en_US
dc.type Article en_US
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