An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Anomaly Detection Using Real-Valued Negative Selection
Genetic Programming and Evolvable Machines
Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm
Genetic Programming and Evolvable Machines
An artificial immune system approach to document clustering
Proceedings of the 2005 ACM symposium on Applied computing
Is negative selection appropriate for anomaly detection?
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
An evolutionary algorithm to generate hyper-ellipsoid detectors for negative selection
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
How Do We Evaluate Artificial Immune Systems?
Evolutionary Computation
A Discrimination Based Artificial Immune System for Classification
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-2 (CIMCA-IAWTIC'06) - Volume 02
Artificial immune systems---today and tomorrow
Natural Computing: an international journal
Revisiting Negative Selection Algorithms
Evolutionary Computation
Application areas of AIS: The past, the present and the future
Applied Soft Computing
Immune system approaches to intrusion detection --- a review
Natural Computing: an international journal
A comparative study of real-valued negative selection to statistical anomaly detection techniques
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Advances in artificial immune systems
IEEE Computational Intelligence Magazine
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
A formal framework for positive and negative detection schemes
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On average time complexity of evolutionary negative selection algorithms for anomaly detection
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Expert Systems with Applications: An International Journal
Artificial immune classifier with swarm learning
Engineering Applications of Artificial Intelligence
Hybridization of immunological computation and fuzzy systems in surgery decision making
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part IV
Run-time malware detection based on positive selection
Journal in Computer Virology
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Artificial Immune Systems (AIS) are a type of intelligent algorithm inspired by the principles and processes of the human immune system. In the last decade, applications of AIS have been studied in various fields. In the application of change/anomaly detection, negative selection algorithms of AIS have been successfully applied. However, negative selection algorithms are not appropriate for multi-class classification problems, because they do not have a mechanism to minimize the danger of overfitting and oversearching. In this paper, we propose a new algorithm to overcome this drawback and to extend the application area of negative selection algorithms to multi-class classification. The algorithm we propose is named Artificial Negative Selection Classifier (ANSC). We investigate the tolerance of ANSC against noise, and introduce a method to reduce the effect of noise into ANSC. The accuracy and data reduction are compared with those from the Artificial Immune Recognition System (AIRS), which is a well known and effective classifier of AIS. The results show that our algorithm is useful for classification problems and the reduction of the noise effect.