@INPROCEEDINGS { Rsds4615,
author = {Medhat, T. and Sammany, Mohammed}, 
title = {Dimensionality Reduction Using Rough Set Approach for Two Neural Networks-Based Applications}, 
booktitle = {Proceedings of the International Conference on Rough Sets and Emerging Intelligent Systems Paradigms (RSEISP'2007)}, 
conference = {International Conference on Rough Sets and Emerging Intelligent Systems Paradigms}, 
series =  = {Lecture Notes in Artificial Intelligence}, 
volume = 4585, 
pages = {639--647}, 
publisher = {Springer-Verlag}, 
address = {Heidelberg - Berlin, Germany}, 
year = 2007, 
editor = {Peters, James F. and Rybinski, Henryk and Skowron, Andrzej}, 
isbn = {978-3-540-73450-5}, 
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.}, 
keywords = {rough sets, neural networks, multi-layered perceptron (mlp), }, 
}