Volume 1, Issue 3 (July 2005)                   IJEEE 2005, 1(3): 1-9 | Back to browse issues page

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S. H. Zahiri, H. Rajabi Mashhadi, S. A. Seyedin. Intelligent and Robust Genetic Algorithm Based Classifier. IJEEE 2005; 1 (3) :1-9
URL: http://ijeee.iust.ac.ir/article-1-52-en.html
Abstract:   (16157 Views)
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.
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Type of Study: Research Paper |
Received: 2008/10/13 | Revised: 2010/10/12 | Accepted: 2010/10/12

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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.