Selection and optimization of cut-points for numeric attribute values

Authors:
L. Shang;S. Y. Yu;X. Y. Jia;Y. S. Ji
Affiliations:
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093, China and Department of Computer Science and Technology, Nanjing University, Nanjing, 210093, China;State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093, China and Department of Computer Science and Technology, Nanjing University, Nanjing, 210093, China;State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093, China and Department of Computer Science and Technology, Nanjing University, Nanjing, 210093, China;State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093, China and Department of Computer Science and Technology, Nanjing University, Nanjing, 210093, China
Venue:
Computers & Mathematics with Applications
Year:
2009

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Abstract

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.