Discretization of Continuous Attributes for Learning Classification Rules
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We present a comparison of three entropy-based discretization methods in a context of learning classification rules. We compare the binary recursive discretization with a stopping criterion based on the Minimum Description Length Principle (MDLP), a non-recursive method which simply chooses a number of cut-points with the highest entropy gains, and a non-recursive method that selects cut-points according to both information entropy and distribution of potential cut-points over the instance space. Our empirical results show that the third method gives the best predictive performance among the three methods tested.
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- Discretization of Continuous Attributes for Learning Classification Rules
- Book Title
- Methodologies for Knowledge Discovery and Data Mining
- Book Subtitle
- Third Pacific-Asia Conference, PAKDD-99 Beijing, China, April 26–28, 1999 Proceedings
- pp 509-514
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- Series Title
- Lecture Notes in Computer Science
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- Springer Berlin Heidelberg
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- Springer-Verlag Berlin Heidelberg
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- Editor Affiliations
- 1. Department of Computer Science and Systems Engineering, Yamaguchi University
- 2. Department of Computer Science and Technology, Tsinghua University
- Author Affiliations
- 5. Department of Computer Science, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada
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