Generalized feature similarity measure

Kamalov, Firuz
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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.
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:
Feature evaluation measures, Feature selection, Information gain, Unified framework, χ2
Kamalov, F. (2020). Generalized feature similarity measure. Annals of Mathematics and Artificial Intelligence.