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| A Multivariate Discretization Method for Continuous Attributes |
| HOU Ju-Chi1, LIANG Ying2, REN Chang-Zhi3 |
1.Department of Student Affairs, Hebei University of Engineering, Hebei 056038
2.Department of Social Work and Social Policy, School of Social and Behavioral Sciences, Nanjing University, Nanjing 210093
3.Department of Precision Instruments and Mechanology, Tsinghua University, Beijing 100084 |
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Abstract Currently, most discretization methods only consider a single variable, which can not get optimal discretization scheme. Taking the relationship among multi-attributes into account, a data discretization method is proposed. A multivariate discretization measurement criterion is presented by means of probabilistic model selection and minimum description length principle (MDLP). An efficient heuristic algorithm is proposed to get the best discretization scheme based on the proposed criterion. Nine UCI datasets are classified and predicted. Experimental results show that the proposed method significantly enhances the learning accuracy of Nave Bayes classifier.
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Received: 06 December 2010
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2011, Vol. 24 
