An integrated approach to filtering phishing e-mails
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
On document classification with self-organising maps
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
A parametric methodology for text classification
Journal of Information Science
Artificial Intelligence Review
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This paper deals with a supervised learning method devoted to producing categorization models of text documents. The goal of the method is to use a suitable numerical measurement of example similarity to find centroids describing different categories of examples. The centroids are not abstract or statistical models, but rather consist of bits of examples. The centroid-learning method is based on a Genetic Algorithm for Texts (GAT). The categorization system using this genetic algorithm infers a model by applying the genetic algorithm to each set of preclassified documents belonging to a category. The models thus obtained are the category centroids that are used to predict the category of a test document. The experimental results validate the utility of this approach for classifying incoming documents.