Abstract
Imbalance in dataset enforces numerous challenges to implementing data analytics in all existing real-world applications using machine learning. Data imbalance occurs when the sample size from a class is very small or large than another class. The performance of predicted models is greatly affected when the dataset is highly imbalanced and the sample size increases. Overall, Imbalanced training data have a major negative impact on performance. Leading machine learning techniques combat the imbalanced dataset by focusing on avoiding the minority class and reducing the inaccuracy for the majority class. This article presents a review of different approaches to classifying imbalanced dataset and their application areas.
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