A combination of discretization and filter methods for improving classification performance in KDD Cup 99 dataset

Authors:
V. Bolón-Canedo;N. Sánchez-Maroño;A. Alonso-Betanzos
Affiliations:
Department of Computer Science, University of A Coruña, Spain;Department of Computer Science, University of A Coruña, Spain;Department of Computer Science, University of A Coruña, Spain
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

KDD Cup 99 dataset is a classical challenge for computer intrusion detection as well as machine learning researchers. Due to the problematic of this dataset, several sophisticated machine learning algorithms have been tried by different authors. In this paper a new approach is proposed that consists in a combination of a discretizator, a filter method and a very simple classical classifier. The results obtained show the adequacy of the method, that achieves comparable or even better performances than those of other more complicated algorithms, but with a considerable reduction in the number of input features. The proposed method has also been tried over another two large datasets maintaining the same behavior as in the KDD Cup 99 dataset.