Abstract: Classifier is a type and can own generalizations, thereby making it feasible to define generalization relationships to other classifiers. In this classifier is a redefinable element, as it is feasible to redefine nested classifiers. In the classification given a set of data representing examples of a target concept make a model to explain the concept and this model classifying future or unknown cases. Classification also estimates the accuracy of the model. This paper presents a survey of current methods for classification designs and the various existing issues. Many tasks that have been conventionally done by humans are now being passed to machines; one may therefore expect there are now an abundance of vision problems posed to computers. In this classifier system is much more than a simple expert system that can learn from experience (which in itself is an immense boon). In this paper design classifier for more than one class and for designing classifier used different operation of genetic programming. The results show that by applying different crossover together with different Mutation increase the performance of the classifier.
Keyword: Genetic Algorithm, classifier, data sets, Genetic Programming, Genetic Operators
Title: A Survey of Classifier Designing Using Genetic Programming and Genetic Operators
Author: A. M. Mansuri, Manish Verma, Pradeep Laxkar,
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