Classification of Imbalanced Data:Review of Methods and Applications

Abstract

Abstract Imbalance in dataset enforces numerous challenges to implement data analytic in all existing real world applications using machine learning. Data imbalance occurs when sample size from a class is very small or large then another class. Performance of predicted models is greatly affected when dataset is highly imbalanced and sample size increases. Overall, Imbalanced training data have a major negative impact on performance. Leading machine learning technique combat with 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 classify imbalanced dataset and their application areas.