A Hybrid Fuzzy Neuro-Immune Network based on Multi-Epitope approach

Abstract

The natural immune system is composed of cells and molecules with complex 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.