A comparative study of one-class classifiers in machine learning problems with extreme class imbalance

Abstract

Classification problems with class imbalance occur when prior probabilities for the data classes differ significantly. The use of one-class classifiers is one of the main approaches to solving such problems. We conduct a comparative study of one-class classification algorithms in classification problems with extreme class imbalance. Emphasis is placed on evaluation of the classificatory accuracy of a one-class classifier based on the Real Valued Negative Selection Algorithm (RVNSA) from Artificial Immune Systems theory, as there are no previous studies focusing on it. Its performance is compared to the performance of 14 alternative classification algorithms which are considered as state of the art in one-class classification problems.