@ARTICLE { Rsds3204,
author = {Ohsuga, Setsuo and Dong, Ju-Zhen}, 
title = {Meningitis data mining by cooperatively using GDT-RS and RSBR}, 
journal = {Pattern Recognition Letters}, 
number = 	{(no. 6)}, 
volume = 24, 
pages = {887-894}, 
publisher = {North Holland}, 
address = {Amsterdam, The Netherlands}, 
month = {March}, 
year = 2003, 
url = {http://www.elsevier.com}, 
issn = {0167-8655}, 
abstract = {This paper describes an application of two rough sets based systems, namely generalized distribution table and rough set (GDT-RS) and rough sets with Boolean reasoning (RSBR) respectively, for mining if-then rules in a meningitis dataset. GDT-RS is a soft hybrid induction system, and RSBR is used for discretization of real valued attributes as a pre-processing step realized before the GDT-RS starts. We argue that discretization of continuous valued attributes is an important pre-processing step in the rule discovery process. We illustrate the quality of rules discovered by GDT-RS is strongly affected by the result of discretization.}, 
keywords = {rough sets, meningitis data mining, class selection, hybrid systems, }, 
}