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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-fa.html
Intelligent and Robust Genetic Algorithm Based Classifier. . 1384; 1 (3) :1-9

URL: http://ijeee.iust.ac.ir/article-1-52-fa.html


چکیده:   (9986 مشاهده)
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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نوع مطالعه: Research Paper |
دریافت: ۱۳۸۷/۷/۲۲

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