Abstract
The Rough Sets Theory, as a powerful knowledge-mining tool, has been widely applied to acquire knowledge in the medical, engineering and financial domains. However, this powerful tool cannot be applied to real-world classification tasks involving continuous features. This requires the utilization of discretization methods. ChiMerge, since it was first proposed in 1992, has become a widely used discretization method. The Chi2 algorithm is one modification to the ChiMerge algorithm. It automates the discretization process by introducing an inconsistency rate as the stopping criterion and it automatically selects the significance level. In addition, it incorporates a finer phase aimed at feature selection to broaden the applications of the ChiMerge algorithm. However, both the ChiMerge and the Chi2 algorithms do not consider the inaccuracy inherent in the merging criterion. In addition, the user-defined inconsistency rate of the Chi2 algorithm also brings about inaccuracy to the discretization process which leads to over-merging. To overcome these two drawbacks, a new discretization method, termed as the modified Chi2 algorithm, is proposed. Comparison studies carried out on the predictive accuracy shows that this modified Chi2 algorithm outperforms the original Chi2 algorithm. Thus, a completely automatic discretization method for Rough Sets Theory has been realized.