Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Self-Organizing Maps and Learning Vector Quantization forFeature Sequences
Neural Processing Letters
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm
Genetic Programming and Evolvable Machines
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
A hybrid fuzzy neuro-immune network based on multi-epitope approach
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
An immunological filter for spam
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Data clustering with a neuro-immune network
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
A comprehensive benchmark of the artificial immune recognition system (AIRS)
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Computers and Industrial Engineering
Information Sciences: an International Journal
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The natural immune system provides an effective defense mechanism against foreign substances via complex interactions among various cells and molecules. Jerne introduced the immune network theory to model the relation between immune cells and molecules. The immune system like the neural system is able to learn from experience. In this paper, a multi-epitopic immune network model is proposed. The proposed model is hybridized with Learning Vector Quantization (LVQ) and fuzzy set theory to present a new supervised learning method. The new method is called Hybrid Fuzzy Neuro-Immune Network based on Multi-Epitope approach (HFNINME). To evaluate the performance of the proposed method several experiments on benchmark classification problems are carried out and the results are compared with two prominent immune-based classifiers as well as several versions of the LVQ algorithm. The results of the experiments reveal that the proposed method yields a parsimonious classifier that can classify data more accurately and more efficiently.