Generating compact classifier systems using a simple artificial immune system.
Current artificial immune system (AIS) classifiers have two major problems: 1) their populations of B-cells can grow to huge proportions, and 2) optimizing one B-cell (part of the classifier) at a time does not necessarily guarantee that the B-cell pool (the whole classifier) will be optimized. In this paper, the design of a new AIS algorithm and classifier system called simple AIS is described. It is different from traditional AIS classifiers in that it takes only one B-cell, instead of a B-cell pool, to represent the classifier. This approach ensures global optimization of the whole system, and in addition, no population control mechanism is needed. The classifier was tested on seven benchmark data sets using different classification techniques and was found to be very competitive when compared to other classifiers.
Leung
Kevin
K
School of Business Information Technology, RMIT University, Melbourne, Vic 3000, Australia. kevin.leung@rmit.edu.au
Cheong
France
F
Cheong
Christopher
C
D000465:Algorithms; D001185:Artificial Intelligence; D032701:Biomimetics / Q000379:methods*; D003198:Computer Simulation; D007113:Immunity, Innate / Q000276:immunology*; D018448:Models, Immunological; D010363:Pattern Recognition, Automated / Q000379:methods*