Deriving a concise description of non-self patterns in an aritificial immune system
New learning paradigms in soft computing
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
On the Surprising Behavior of Distance Metrics in High Dimensional Spaces
ICDT '01 Proceedings of the 8th International Conference on Database Theory
Coverage and Generalization in an Artificial Immune System
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Rough Set Data Mining of Diabetes Data
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Generating Optimal Repertoire of Antibody Strings in an Artificial Immune System
Proceedings of the IIS'2000 Symposium on Intelligent Information Systems
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Architecture for an Artificial Immune System
Evolutionary Computation
Introduction to Data Compression, Third Edition (Morgan Kaufmann Series in Multimedia Information and Systems)
Revisiting Negative Selection Algorithms
Evolutionary Computation
Theoretical advances in artificial immune systems
Theoretical Computer Science
Phase transition and the computational complexity of generating r-contiguous detectors
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
A sense of self for Unix processes
SP'96 Proceedings of the 1996 IEEE conference on Security and privacy
An artificial immune system architecture for computer securityapplications
IEEE Transactions on Evolutionary Computation
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This paper describes a b-v model which is enhanced version of the negative selection algorithm (NSA). In contrast to formerly developed approaches, binary and real-valued detectors are simultaneously used. The reason behind developing this hybrid is our willingness to overcome the scalability problems occuring when only one type of detectors is used. High-dimensional datasets are a great challenge for NSA. But the quality of generated detectors, duration of learning stage as well as duration of classification stage need a careful treatment also. Thus, we discuss various versions of the b-v model developed to increase its efficiency. Versatility of proposed approach was intensively tested by using popular testbeds concerning domains like computer's security (intruders and spam detection) and recognition of handwritten words.