Alleviating class imbalance using data sampling: Examining the effects on classification algorithms

Imbalanced class distributions typically cause poor classifier performance on the minority class, which also tends to be the class with the highest cost of mis-classification. Data sampling is a common solution to this problem, and numerous sampling techniques have been proposed to address it. Prior research examining the performance of these techniques has been narrow and limited. This work uses thorough empirical experimentation to compare the performance of seven existing data sampling techniques using five different classifiers and four different datasets. The work addresses which sampling techniques produce the best performance in the presence of class unbalance, which classifiers are most robust to the problem, as well as which sampling techniques perform better or worse with each classifier. Extensive statistical analysis of these results is provided, in addition to an examination of the qualitative effects of the sampling techniques on the types of predictions made by the C4.5 classifier.