Artificial immune system-based classification in class-imbalanced problems

We investigate the effect of the Class Imbalance Problem on the performance of an Artificial Immune System(AIS)-based classification algorithm. Our motivation stems from the fact that the Adaptive Immune System constitutes one of the most sophisticated biological systems which is particularly evolved in order to continuously address an extremely unbalanced pattern classification problem. That is the "self"/"non-self" discrimination process, consisting in classifying any cell as "self" or "non-self". Our experimentation indicates that the AIS-based classification paradigm has the intrinsic properly in dealing more efficiently with highly skewed datasets than standard pattern classification algorithms such as the Support Vector Machines (SVMs). Specifically, the experimental results presented in this paper provide justifications concerning the superiority of AISbased classification in identifying instances from the minority class.