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Term evaluation metrics in imbalanced text categorization

Published online by Cambridge University Press:  12 July 2019

Behzad Naderalvojoud Open the ORCID record for Behzad Naderalvojoud [Opens in a new window]  and
Ebru Akcapinar Sezer
Behzad Naderalvojoud*
Affiliation:
Department of Computer Engineering, Hacettepe University, 06800, Ankara, Turkey
Ebru Akcapinar Sezer
Affiliation:
Department of Computer Engineering, Hacettepe University, 06800, Ankara, Turkey
*
*Corresponding author. Emails: [email protected], [email protected]
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Abstract

This paper proposes four novel term evaluation metrics to represent documents in the text categorization where class distribution is imbalanced. These metrics are achieved from the revision of the four common term evaluation metrics: chi-square, information gain, odds ratio, and relevance frequency. While the common metrics require a balanced class distribution, our proposed metrics evaluate the document terms under an imbalanced distribution. They calculate the degree of relatedness of terms with respect to minor and major classes by considering their imbalanced distribution. Using these metrics in the document representation makes a better distinction between the documents of the minor and major classes and improves the performance of machine learning algorithms. The proposed metrics are assessed over three popular benchmarks (two subsets of Reuters-21578 and WebKB) by using four classification algorithms: support vector machines, naive Bayes, decision trees, and centroid-based classifiers. Our empirical results indicate that the proposed metrics outperform the common metrics in the imbalanced text categorization.

Keywords

Text classificationClass imbalance problemTerm evaluationMachine learning
Type
Article
Information
Natural Language Engineering , Volume 26 , Issue 1 , January 2020 , pp. 31 - 47
Copyright
© Cambridge University Press 2019 

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