Selection and optimization of cut-points for numeric attribute values

✍ Scribed by L. Shang; S.Y. Yu; X.Y. Jia; Y.S. Ji

• Publisher: Elsevier Science
• Year: 2009
• Tongue: English
• Weight: 664 KB
• Volume: 57
• Category: Article
• ISSN: 0898-1221
• DOI: 10.1016/j.camwa.2008.10.039

✦ Synopsis

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.