An investigation into the source of power for AIRS, an artificial immune classification system

The AIRS classifier, based on metaphors from the field of artificial immune systems, has shown itself to be an effective general purpose classifier across a broad spectrum of classification problems. This research examines the new classifier empirically, replacing one of the two likely sources of its classification power with alternative modifications. The results are slightly less effective, but not statistically significantly so. We conclude that the modifications, which are computationally somewhat more efficient, provide fast test versions of AIRS for users to experiment with. We also conclude that the chief source of classification power of AIRS must lie in its replacement and maintenance of its memory cell population.

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