A UMDA-Based Discretization Method for Continuous Attributes

Abstract:

Discretization of continuous attributes have played an important role in machine learning and data mining. They can not only improve the performance of the classifier, but also reduce the space of the storage. Univariate Marginal Distribution Algorithm is a modified Evolutionary Algorithms, which has some advantages over classical Evolutionary Algorithms such as the fast convergence speed and few parameters need to be tuned. In this paper, we proposed a bottom-up, global, dynamic, and supervised discretization method on the basis of Univariate Marginal Distribution Algorithm.The experimental results showed that the proposed method could effectively improve the accuracy of classifier.

• Periodical: Advanced Materials Research (Volumes 403-408)
• Pages: 1834-1838
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.403-408.1834
• Online since: November 2011
• Authors: Jing Zhao, Chong Zhao Han, Bin Wei, De Qiang Han
• Keywords: Data Mining (DM), Discretization, Univariate Marginal Distribution Algorithm

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