Genetic Implementation of a Classifier Based on Data Separation by means of Hyperspheres
نویسندگان
چکیده
This paper discusses a genetic implementation of the growing hyperspheres classifier (GHS) for highdimensional data classification. The main idea of the GHS classifier consists in data separation by n-dimensional hyperspheres properly spread over the training data. First, the idea of training data representation is described. Then a brief description of a previous first representation by neural networks is reminded. The main part of this paper is focused on a precise description of the classifier implementation by genetic algorithms. Features of the new approach are discussed and compared. Finally, a task classifying data from a gamma telescope is presented to show the capabilities of the classifier.
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