Instance-Based Learning Algorithms
Machine Learning
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
Don't push me! Collision-avoiding swarms
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Application areas of AIS: The past, the present and the future
Applied Soft Computing
Theoretical advances in artificial immune systems
Theoretical Computer Science
A negative selection algorithm for classification and reduction of the noise effect
Applied Soft Computing
Use of particle swarm optimization for machinery fault detection
Engineering Applications of Artificial Intelligence
Accurate diagnosis of induction machine faults using optimal time-frequency representations
Engineering Applications of Artificial Intelligence
Advances in artificial immune systems
IEEE Computational Intelligence Magazine
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Generating Compact Classifier Systems Using a Simple Artificial Immune System
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
International Journal of Data Analysis Techniques and Strategies
Engineering Applications of Artificial Intelligence
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Artificial immune systems are computational systems inspired by the principles and processes of the natural immune system. The various applications of artificial immune systems have been used for pattern recognition and classification problems; however, these artificial immune systems have three major problems, which are growing of the memory cell population, eliminating of the useful memory cells in next the steps, and randomly using cloning and mutation operators. In this study, a new artificial immune classifier with swarm learning is proposed to solve these three problems. The proposed algorithm uses the swarm learning to evolve the antibody population. In each step, the antibodies that belong to the same class move to the same way according to their affinities. The size of the memory cell population does not grow during the training stage of the algorithm. Therefore, the method is faster than other artificial immune classifiers. The classifier was tested on two case studies. In the first case study, the algorithm was used to diagnose the faults of induction motors. In the second case study, five benchmark data sets were used to evaluate the performance of the algorithm. The results of second case studies show that the proposed method gives better results than two well-known artificial immune systems for real word data sets. The results were compared to other classification techniques, and the method is competitive to other classifiers.