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Generalized feature similarity measure

Generalized feature similarity measure 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Mathematics and Artificial Intelligence Springer Journals

Generalized feature similarity measure

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References (24)

Publisher
Springer Journals
Copyright
Copyright © Springer Nature Switzerland AG 2020
ISSN
1012-2443
eISSN
1573-7470
DOI
10.1007/s10472-020-09700-8
Publisher site
See Article on Publisher Site

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.

Journal

Annals of Mathematics and Artificial IntelligenceSpringer Journals

Published: Sep 20, 2020

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