Auteurs: | » FARAOUN Kamel Mohamed » BOUKELIF Aoued | |
Type : | Revue Internationale | |
Nom du journal : | International Journal of Computer and Information Engineering ISSN: | |
Volume : 1 | Issue: 10 | Pages: 3111-3122 |
Lien : » | ||
Publié le : | 29-10-2007 |
This paper describes a new approach of classification using genetic programming. The proposed technique consists of genetically coevolving a population of non-linear transformations on the input data to be classified, and map them to a new space with a reduced dimension, in order to get a maximum inter-classes discrimination. The classification of new samples is then performed on the transformed data, and so become much easier. Contrary to the existing GP-classification techniques, the proposed one use a dynamic repartition of the transformed data in separated intervals, the efficacy of a given intervals repartition is handled by the fitness criterion, with a maximum classes discrimination. Experiments were first performed using the Fisher-s Iris dataset, and then, the KDD-99 Cup dataset was used to study the intrusion detection and classification problem. Obtained results demonstrate that the proposed genetic …