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Learning From Imbalanced Data

Learning From Imbalanced Data

Lincy Mathews, Seetha Hari
Copyright: © 2018 |Pages: 10
ISBN13: 9781522522553|ISBN10: 1522522557|EISBN13: 9781522522560
DOI: 10.4018/978-1-5225-2255-3.ch159
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MLA

Mathews, Lincy, and Seetha Hari. "Learning From Imbalanced Data." Encyclopedia of Information Science and Technology, Fourth Edition, edited by Mehdi Khosrow-Pour, D.B.A., IGI Global, 2018, pp. 1825-1834. https://doi.org/10.4018/978-1-5225-2255-3.ch159

APA

Mathews, L. & Hari, S. (2018). Learning From Imbalanced Data. In M. Khosrow-Pour, D.B.A. (Ed.), Encyclopedia of Information Science and Technology, Fourth Edition (pp. 1825-1834). IGI Global. https://doi.org/10.4018/978-1-5225-2255-3.ch159

Chicago

Mathews, Lincy, and Seetha Hari. "Learning From Imbalanced Data." In Encyclopedia of Information Science and Technology, Fourth Edition, edited by Mehdi Khosrow-Pour, D.B.A., 1825-1834. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-2255-3.ch159

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Abstract

A very challenging issue in real world data is that in many domains like medicine, finance, marketing, web, telecommunication, management etc., the distribution of data among classes is inherently imbalanced. A widely accepted researched issue is that the traditional classifier algorithms assume a balanced distribution among the classes. Data imbalance is evident when the number of instances representing the class of concern is much lesser than other classes. Hence, the classifiers tend to bias towards the well-represented class. This leads to a higher misclassification rate among the lesser represented class. Hence, there is a need of efficient learners to classify imbalanced data. This chapter aims to address the need, challenges, existing methods and evaluation metrics identified when learning from imbalanced data sets. Future research challenges and directions are highlighted.

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