Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Boggess, Lois C.

Committee Member

Reese, Donna S.

Committee Member

Vaughn, Rayford B.

Committee Member

Bridges, Susan M.

Date of Degree

12-14-2001

Document Type

Graduate Thesis - Open Access

Major

Computer Science

Degree Name

Master of Science

College

College of Engineering

Department

Department of Computer Science

Abstract

The natural immune system embodies a wealth of information processing capabilities that can be exploited as a metaphor for the development of artificial immune systems. Chief among these features is the ability to recognize previously encountered substances and to generalize beyond recognition in order to provide appropriate responses to pathogens not seen before. This thesis presents a new supervised learning paradigm, resource limited artificial immune classifiers, inspired by mechanisms exhibited in natural and artificial immune systems. The key abstractions gleaned from these immune systems include resource competition, clonal selection, affinity maturation, and memory cell retention. A discussion of the progenitors of this work is offered. This work provides a thorough explication of a resource limited artifical immune classification algorithm, named AIRS (Artificial Immune Recognition System). Experimental results on both simulated data sets and real world machine learning benchmarks demonstrate the effectiveness of the AIRS algorithm as a classification technique.

URI

https://hdl.handle.net/11668/19122

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