Artificial Immune Recognition System (AIRS): Revisions and Refinements

This paper revisits the Artificial Immmune Recognition System (AIRS) that has been developed as an immune-inspired supervised learning algorithm. Certain unnecessary complications of the original algorithm are discussed and means of overcomming these complexities are proposed. Experimental evidence is presented to support these revisions which do not sacrifice the accuracy of the original algorihtm but, rather, maintain accuracy whilst increasing the simplicity and data reduction capabilities of AIRS.

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