A Discretization Algorithm Based on Gini Criterion

In this paper, a supervised, global and static algorithm for the discretization of continuous attributes is presented. This algorithm takes account of the distribution of class probability vector by applying the Gini criterion. The proposed discretization method is compared with Ent-MDLP, which is known as one of the best discretization methods, in terms of predictive error rate and tree size. This paper reveals that the proposed algorithm is effective and can be a good alternative to the entropy-based discretization methods in some situations.