Artificial Immune System-based Classification in Class-Imbalanced Image Classification Problems

In this paper, we compare the performance of Artificial Immune System (AIS)-based classification algorithms to the performance of Gaussian kernel-based Support Vector Machines (SVM) in problems with a high degree of class imbalance. Our experimentation indicates that the AIS-based classification paradigm has the intrinsic properly of dealing more efficiently with highly skewed datasets. Specifically, our experimental results indicate that AIS-based classifiers identify instances from the minority class quite efficiently.