Methodologies from machine learning in data analysis and software
The Computer Journal - Special issue on distributed systems
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Computer Methods and Programs in Biomedicine
MILA: multilevel immune learning algorithm
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Proceedings of the 6th international conference on Artificial immune systems
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
Two ways to grow tissue for artificial immune systems
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Designing ensembles of fuzzy classification systems: an immune-inspired approach
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Application areas of AIS: the past, the present and the future
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
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Due to the fact that there exist only a small number of complex systems in artificial immune systems (AISs) that solve nonlinear problems, there is a need to develop nonlinear AIS approaches thatwould be among the well-known solution methods. In this study, we developed a kernel-based AIS to compensate for this deficiency by providing a nonlinear structure via transformation of distance calculations in the clonal selection models of classical AIS to kernel space. Applications of the developed system were conducted on Statlog heart disease dataset, which was taken from the University of California, Irvine Machine-Learning Repository, and on Doppler sonograms to diagnose atherosclerosis disease. The system obtained a classification accuracy of 85.93% for the Statlog heart disease dataset, while it achieved a 99.09% classification success for the Doppler dataset. With these results, our system seems to be a potential solution method, and it may be considered as a suitable method for hard nonlinear classification problems.