Data reduction: discretization of numerical attributes

Discretization is the process of converting a numerical attribute into a symbolic attribute by partitioning the attribute domain. This chapter presents the taxonomy of currently developed discretization systems. Discretization techniques based on equal interval frequency, equal interval width, minimum class entropy, minimum description length, and clustering are briefly described. In addition, other methods of discretization are also outlined.

[1]  Ronald L. Rivest,et al.  Inferring Decision Trees Using the Minimum Description Length Principle , 1989, Inf. Comput..

[2]  Caroline Chan,et al.  Determination of quantization intervals in rule based model for dynamic systems , 1991, Conference Proceedings 1991 IEEE International Conference on Systems, Man, and Cybernetics.

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

[4]  Andrew K. C. Wong,et al.  Information Discovery through Hierarchical Maximum Entropy Discretization and Synthesis , 1991, Knowledge Discovery in Databases.

[5]  Jerzy W. Grzymala-Busse,et al.  LERS-A System for Learning from Examples Based on Rough Sets , 1992, Intelligent Decision Support.

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

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

[8]  Thierry Van de Merckt Decision Trees in Numerical Attribute Spaces , 1993, IJCAI.

[9]  Bernhard Pfahringer,et al.  Compression-Based Discretization of Continuous Attributes , 1995, ICML.

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

[11]  J. Ross Quinlan,et al.  Improved Use of Continuous Attributes in C4.5 , 1996, J. Artif. Intell. Res..

[12]  Nir Friedman,et al.  Discretizing Continuous Attributes While Learning Bayesian Networks , 1996, ICML.

[13]  Ron Kohavi,et al.  Error-Based and Entropy-Based Discretization of Continuous Features , 1996, KDD.

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

[15]  Andrzej Skowron,et al.  Boolean Reasoning for Feature Extraction Problems , 1997, ISMIS.

[16]  Paul D. Scott,et al.  Zeta: A Global Method for Discretization of Continuous Variables , 1997, KDD.

[17]  Djamel A. Zighed,et al.  Optimal Multiple Intervals Discretization of Continuous Attributes for Supervised Learning , 1997, KDD.

[18]  Ramesh Subramonian,et al.  A Visual Interactive Framework for Attribute Discretization , 1997, KDD.

[19]  Hung Son Nguyen,et al.  Discretization Problem for Rough Sets Methods , 1998, Rough Sets and Current Trends in Computing.

[20]  Jerzy Stefanowski,et al.  Handling Continuous Attributes in Discovery of Strong Decision Rules , 1998, Rough Sets and Current Trends in Computing.

[21]  Wen Gao,et al.  The Discretization of Continuous Attributes Based on Compatibility Rough Set and Generic Algorithm , 1999, RSFDGrC.

[22]  Usama M. Fayyad,et al.  On the Handling of Continuous-Valued Attributes in Decision Tree Generation , 1992, Machine Learning.