Showing 1 results for S. H. Zahiri
S. H. Zahiri, H. Rajabi Mashhadi, S. A. Seyedin,
Volume 1, Issue 3 (July 2005)
Abstract
The concepts of robust classification and intelligently controlling the search
process of genetic algorithm (GA) are introduced and integrated with a conventional
genetic classifier for development of a new version of it, which is called Intelligent and
Robust GA-classifier (IRGA-classifier). It can efficiently approximate the decision
hyperplanes in the feature space.
It is shown experimentally that the proposed IRGA-classifier has removed two important
weak points of the conventional GA-classifiers. These problems are the large number of
training points and the large number of iterations to achieve a comparable performance with
the Bayes classifier, which is an optimal conventional classifier.
Three examples have been chosen to compare the performance of designed IRGA-classifier
to conventional GA-classifier and Bayes classifier. They are the Iris data classification, the
Wine data classification, and radar targets classification from backscattered signals. The
results show clearly a considerable improvement for the performance of IRGA-classifier
compared with a conventional GA-classifier.