Selection and optimization of cut-points for numeric attribute values

Data discretization is the process of setting several cut-points which can represent attribute values using different symbols or integer values for continuous numeric attribute values. A hybrid method based on neural network and genetic algorithm is proposed to select and optimize the cut-points for numeric attribute values. The values of cuts are trained through the four-layer neural network and the number of cut-points is optimized by the genetic algorithm. The results for intervals through the presented method can be more precise. The experimental results show that the cut-points are well obtained compared with the other method.