A Novel Weightless Artificial Neural Based Multi-Classifier for Complex Classifications

Authors:
P. Lorrentz;W. G. Howells;K. D. Mcdonald-Maier
Affiliations:
Department of Electronics, University of Kent, Canterbury, UK CT2 7NT;Department of Electronics, University of Kent, Canterbury, UK CT2 7NT;Department of Computing and Electronic Systems, University of Essex, Colchester, UK CO4 3SQ
Venue:
Neural Processing Letters
Year:
2010

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Abstract

Artificial neural systems in general and weightless systems in particular, have traditionally struggled in performance terms when confronted with problem domains such as possessing a large number of independent pattern classes and pattern classes with non-standard distributions. A multi-classifier is proposed which explores problem domains with a large number of independent pattern classes typically found in forensic and security databases. Specifically, the multi-classifier system is demonstrated on the exemplar of fingerprint identification problem typical to forensic, biometric, and security. Furthermore, the multi-classifier is able to provide a reasonable solution to benchmark problems from medicinal and physical (science) fields, which are determining the health, state of thyroid glands and determining whether or not there is a structure in the ionosphere, respectively.