Abstract—A supervised, global and static algorithm for the discretization of continuous features is presented in this paper. The proposed discretization algorithm extends Gini-criterion concept. The algorithm hybridizes binning, and entropy method based on the amendment from 1R and Ent-Minimum Description Length Principle (MDLP) method. The proposed algorithm is comparable due to its simplicity, yet need further improvement and testing using various data sets. The proposed algorithm can be a good alternative method in the entropy-based discretization family.
Index Terms—Continuous, discretization, entropy, Gini criterion.
Authors are with the School of Electrical and Electronic Engineering, Universiti Sains Malaysia, 14300 Penang, Malaysia (e-mail: yasmin160273@ yahoo.com; firstname.lastname@example.org; email@example.com).
Cite: Yasmin Mohd Yacob, Harsa Amylia Mat Sakim, and Nor Ashidi Mat Isa, "A Discretization Algorithm Based on Extended Gini Criterion," International Journal of Machine Learning and Computing vol. 2, no. 3, pp. 209-212, 2012.