Supervised Discretization for Optimal Prediction

Abstract When data are high dimensional with a response variable categorical and explanatory variables mix-typed, a conveniently exe- cutable profile usually consists of categorical or categorized variables. This requires changing continuous variables to categorical variables. A supervised discretization algorithm for optimal prediction (with the GK-lambda) is proposed. The comparison of this algorithm with the supervised discretization for proportional prediction proposed in 1 is shown. Tests with some data sets from Machine Learning Repository(UCI) are presented.

[1]  Jianhong Wu,et al.  Supervised Discretization with GK - τ , 2013, ITQM.

[2]  Usama M. Fayyad,et al.  Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning , 1993, IJCAI.

[3]  Robert C. Holte,et al.  Very Simple Classification Rules Perform Well on Most Commonly Used Datasets , 1993, Machine Learning.

[4]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[5]  Irina Rish,et al.  An empirical study of the naive Bayes classifier , 2001 .

[6]  S. Kotsiantis,et al.  Discretization Techniques: A recent survey , 2006 .

[7]  L. A. Goodman,et al.  Measures of association for cross classifications , 1979 .

[8]  Andrew K. C. Wong,et al.  Information synthesis based on hierarchical maximum entropy discretization , 1990, J. Exp. Theor. Artif. Intell..

[9]  Jason Catlett,et al.  On Changing Continuous Attributes into Ordered Discrete Attributes , 1991, EWSL.

[10]  Randy Kerber,et al.  ChiMerge: Discretization of Numeric Attributes , 1992, AAAI.

[11]  Jerzy W. Grzymala-Busse,et al.  Global discretization of continuous attributes as preprocessing for machine learning , 1996, Int. J. Approx. Reason..

[12]  David A. Landgrebe,et al.  A survey of decision tree classifier methodology , 1991, IEEE Trans. Syst. Man Cybern..

[13]  Guojun Gan,et al.  1. Data Clustering , 2007 .

[14]  Ron Kohavi,et al.  Supervised and Unsupervised Discretization of Continuous Features , 1995, ICML.

[15]  Marc Boullé,et al.  Khiops: A Statistical Discretization Method of Continuous Attributes , 2004, Machine Learning.

[16]  Huan Liu,et al.  Chi2: feature selection and discretization of numeric attributes , 1995, Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence.

[17]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[18]  Nominal Association Vector and Matrix , 2011, 1109.2553.

[19]  Guojun Gan,et al.  Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability) , 2007 .