Supervised classification algorithms based on artificial immune

Abstract

In order to explore more efficient classification method, this paper presents a supervised classification algorithm based on artificial immune. It describes the representation of antibody and antigen in the classification algorithm, mathematical model of antibody population reproduction and immune memory formation. The experimental results show that the algorithm can achieve high classification performance. The average classification accuracy is 89.3%, stable classification performance. It has non-linear and clone selection, immune regulation, immune memory and other features of biological immune system, which provides a new solution for supervised classification problem.