Comparison Of The Different Sampling Techniques For Imbalanced Classification Problems In Machine Learning

Imbalanced class distribution is a scenario where the number of observations belonging to one class is significantly lower than those belonging to the other ones. Machine learning algorithms are often designed to improve accuracy by reducing the errors. Thus, they do not consider the class distribution proportion or the balance of classes. In this paper, firstly, we describes the various approaches for solving such class imbalance problems, using various sampling techniques. Then we weigh each technique for its pros and cons. Finally, an approach purpose is revealed in which you can create a balanced class distribution and apply ensemble learning technique designed especially for imbalanced class distribution.