A novel multi-epitopic immune network model hybridized with neural theory and fuzzy concept.
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.
Izadinia
Hamid
H
Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran. izadinia@aut.ac.ir
Sadeghi
Fereshteh
F
Ebadzadeh
Mohammad Mehdi
MM
D000465:Algorithms; D000906:Antibodies / Q000378:metabolism; D001185:Artificial Intelligence; D003198:Computer Simulation; D016208:Databases, Factual; D017143:Fuzzy Logic; D006801:Humans; D018448:Models, Immunological; D008959:Models, Neurological; D016571:Neural Networks, Computer
D000906:Antibodies