| Summary: | Many pattern classification tasks confront with the problem that may have a very high dimensional
feature space like in Intrusion Detection System (IDS) data. Rough set is widely used in IDS to overcome the
arising issue. In rough set, there are several stage processing including discretization part which is a vital task in data mining, particularly in the classification problem. Two results distinguish showing that the discretization stage is hugely important in both training and testing of IDS data. In proposed framework, analysis should been done to discretization, reduct and rules stage to determine the significant algorithm and core element in IDS data. The classification using standard voting, since it is a rule-based classification.
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