Learning From Imbalanced Data

Learning From Imbalanced Data

Lincy Mathews, Seetha Hari
Copyright: © 2019 |Pages: 12
DOI: 10.4018/978-1-5225-7598-6.ch030
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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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Characteristics Of Imbalanced Data

The imbalance ratio between the majority and minority instances need not necessarily affect the performance of classifiers if the degree of imbalance is moderate (Chen & Wasikowski, 2008). The inherent characteristics within minority data however; can cause degrade in performance by the learning models. Two basic categorization of minority instances exist; Safe and unsafe minority instances. Safe minority instances are instances where the misclassification is minimal by the base learners. These instances exist much away from the borderline of majority instances. Unsafe minority instances are so called because the misclassifications occur highly with these kinds of minority instances.

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