  The problem of class imbalance is extensive for focusing on numerous
applications in the real world. In such a situation, nearly all of the examples
are labeled as one class called majority class, while far fewer examples are
labeled as the other class usually, the more important class is called
minority. Over the last few years, several types of research have been carried
out on the issue of class imbalance, including data sampling, cost-sensitive
analysis, Genetic Programming based models, bagging, boosting, etc.
Nevertheless, in this survey paper, we enlisted the 24 related studies in the
years 2003, 2008, 2010, 2012 and 2014 to 2019, focusing on the architecture of
single, hybrid, and ensemble method design to understand the current status of
improving classification output in machine learning techniques to fix problems
with class imbalances. This survey paper also includes a statistical analysis
of the classification algorithms under various methods and several other
experimental conditions, as well as datasets used in different research papers.
