Class Imbalance Learning

Data classication task assigns labels to data points using a model that is learned from a collection of pre-labeled data points. The Class Imbalance Learning (CIL) problem is concerned with the performance of classication algorithms in the presence of under-represented data and severe class distribution skews. Due to the inherent complex characteristics of imbalanced datasets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data effciently into information and knowledge representation. It is important to study CIL because it is rare to nd a classication problem in real world scenarios that follows balanced class distributions. In this article, we have presented how machine learning has become the integral part of modern lifestyle and how some of the real world problems are modeled as CIL problems. We have also provided a detailed survey on the fundamentals and solutions to class imbalance learning. We conclude the survey by presenting some of the challenges and opportunities with class imbalance learning.