Pattern Recognition using Artificial Immune System

TL;DR

This thesis explores the application of Artificial Immune Systems in machine learning, introduces a new unsupervised classification algorithm inspired by immune principles, and demonstrates its effectiveness through experiments.

Contribution

A novel, nearly parameter-free immune-inspired unsupervised classification algorithm called UCSC is proposed, which adapts data-driven parameters for improved performance.

Findings

UCSC performs well in classification tasks.

The algorithm is more reliable than existing methods.

UCSC adapts parameters automatically for efficiency.

Abstract

In this thesis, the uses of Artificial Immune Systems (AIS) in Machine learning is studded. the thesis focus on some of immune inspired algorithms such as clonal selection algorithm and artificial immune network. The effect of changing the algorithm parameter on its performance is studded. Then a new immune inspired algorithm for unsupervised classification is proposed. The new algorithm is based on clonal selection principle and named Unsupervised Clonal Selection Classification (UCSC). The new proposed algorithm is almost parameter free. The algorithm parameters are data driven and it adjusts itself to make the classification as fast as possible. The performance of UCSC is evaluated. The experiments show that the proposed UCSC algorithm has a good performance and more reliable.