Abstract Since rough sets theory unveils the dependency of data and implements data reduction, it has attracted much attention from more and more fields. Moreover, discretization of continuous attributes plays an important role in rough sets theory and other induction learning systems. Because discretization is viewed as a process of information generalization (or abstraction) and data reduction, a global discretization algorithm is proposed based on rough sets theory. It modifies the criterion of selecting the best cut points, and introduces inconsistency checking to preserve the fidelity of the original data,which changes the MDLP method into a global one. Then the reduction of cut points is performed to lead to small size learning model while keeping the consistency level. The proposed algorithm is tested on several data sets with ID3 and ROSETTA. Experimental results show that this method performs better than MDLP and it is also superior to processing continuous data directly without discretization.
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2006, Vol. 19 
