Deriving a concise description of non-self patterns in an aritificial immune system
New learning paradigms in soft computing
AINE: An Immunological Approach to Data Mining
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Immunochip Architecture and Its Emulation
EH '02 Proceedings of the 2002 NASA/DoD Conference on Evolvable Hardware (EH'02)
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
An Immunological Approach to Change Detection: Algorithms, Analysis and Implications
SP '96 Proceedings of the 1996 IEEE Symposium on Security and Privacy
Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm
Genetic Programming and Evolvable Machines
How Do We Evaluate Artificial Immune Systems?
Evolutionary Computation
Negative Selection based method for Multi-Class problem classification
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
A new classifier based on resource limited artificial immune systems
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Advances in artificial immune systems
IEEE Computational Intelligence Magazine
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
An Expanded Lateral Interactive Clonal Selection Algorithm and Its Application
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
An immune inspired co-evolutionary affinity network for prefetching of distributed object
Journal of Parallel and Distributed Computing
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In the paper a new classification method is proposed. It is based on Negative Selection, which was originally designed for anomaly detection and dichotomic classification. In our earlier work we described M-NSA algorithm that can be applied in multi-class classification problems. Trying to improve classification accuracy of M-NSA we propose a new version of this algorithm, called MINSA, where refinement of receptors set is applied. The accuracy of MINSA was tested in an experimental way with the use of benchmark data sets. The experiments confirmed that direction of changes introduced in MINSA improves its accuracy in comparison to M-NSA. Comparison with other methods of classification is also shown in the paper.