Paper Info
Title
A hybrid fuzzy neuro-immune network based on multi-epitope approach
Abstract
The natural immune system is composed of cells and molecules with com plex interactions. Jerne modeled the interactions among immune cells and molecules by introducing the immune network. The immune system provides an effective defense mechanism against foreign substances. This system like the neural system is able to learn from experience. In this paper, the Jerne's immune network model is extended and a new classifier based on the new immune network model and Learning Vector Quantization (LVQ) is proposed. The new classification method is called Hybrid Fuzzy Neuro-Immune Network based on Multi-Epitope approach (HFNINME). The performance of the proposed method is evaluated via several benchmark classification problems and is compared with two other prominent immune-based classifiers. The experiments reveal that the proposed method yields a parsimonious classifier that can classify data more accurately and more efficiently.
Year
DOI
Venue
2009
10.1109/IJCNN.2009.5178810
Atlanta, GA
Keywords
Field
DocType
immune network model,new immune network model,immune system,hybrid fuzzy neuro-immune network,immune network,immune cell,new classification method,natural immune system,multi-epitope approach,neural system,new classifier,artificial immune systems,learning vector quantization,data mining,fuzzy set theory,defense mechanism,learning artificial intelligence,network model,decoding,kinematics,shape,force,molecules,cloning,neurophysiology,immune cells,information analysis
Epitope,Artificial immune system,Immune network,Pattern recognition,Computer science,Fuzzy logic,Learning vector quantization,Fuzzy set,Artificial intelligence,Immune system,Classifier (linguistics),Machine learning
Conference
ISSN
ISBN
Citations 
1098-7576 E-ISBN : 978-1-4244-3553-1
978-1-4244-3553-1
2
PageRank 
References 
Authors
0.41
2
3
Name
Order
Citations
PageRank
Hamid Izadinia116411.16
Fereshteh Sadeghi21005.65
Mohammad Mehdi Ebadzadeh337227.36