An Evolutionary Immune Network for Data Clustering
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Supervised fuzzy clustering for the identification of fuzzy classifiers
Pattern Recognition Letters
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Structural design of the danger model immune algorithm
Information Sciences: an International Journal
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Self-nonself discrimination has long been the fundamental model of modern theoretical immunology. Based on this principle, some effective and efficient artificial immune algorithms have been proposed and applied to a wide range of engineering applications. Over the last few years, a new model called ''danger theory'' has been developed to challenge the classical self-nonself model. In this paper, a novel immune algorithm inspired by danger theory is proposed for solving on-line supervised two-class classification problems. The general framework of the proposed algorithm is described, and several essential issues related to the learning process are also discussed. Experiments based on both artificial data sets and real-world problems are carried out to visualize the learning process, as well as to evaluate the classification performance of our method. It is shown empirically by the experimental results that the proposed algorithm exhibits competitive classification accuracy and generalization capability.