Dimensionality Reduction Using Rough Set Approach for Two Neural Networks-Based Applications

Authors:
Mohammed Sammany;T. Medhat
Affiliations:
National Water Research Center Ministry of Water Resources and Irrigation, Kornish El-Nile, Imbaba-Giza 12666- Cairo, Egypt;Department of Physics and Engineering Mathematics, Faculty of Engineering,Tanta University, 31521, Tanta, Egypt
Venue:
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Year:
2007

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Abstract

In this paper, Rough Sets approach has been used to reduce the number of inputs for two neural networks-based applications that are, diagnosing plant diseases and intrusion detection. After the reduction process, and as a result of decreasing the complexity of the classifiers, the results obtained using Multi-Layer Perceptron (MLP) revealed a great deal of classification accuracy without affecting the classification decisions.