C4.5: programs for machine learning
C4.5: programs for machine learning
General and Efficient Multisplitting of Numerical Attributes
Machine Learning
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Genetic Algorithm and Graph Partitioning
IEEE Transactions on Computers
Class-Dependent Discretization for Inductive Learning from Continuous and Mixed-Mode Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Changing Continuous Attributes into Ordered Discrete Attributes
EWSL '91 Proceedings of the European Working Session on Machine Learning
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Fuzzy Data Mining: Effect of Fuzzy Discretization
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Discretization and Grouping: Preprocessing Steps for Data Mining
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Fuzzy Rule Selection By Data Mining Criteria And Genetic Algorithms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Interval-valued fuzzy hypergraph and fuzzy partition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Feature Selection in Genetic Fuzzy Discretization for the Pattern Classification Problems
IEICE - Transactions on Information and Systems
Discretization method of continuous attributes based on decision attributes
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II
Retail clients latent segments
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
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We propose a genetic fuzzy discretization method for continuous numerical attributes. Traditional discretization methods categorize the continuous attributes into a number of bins. Because they are made on crisp discretization, there exists considerable information loss. Fuzzy discretization allows overlapping intervals and reflects linguistic classification. However, the number of intervals, the boundaries of intervals, and the degrees of overlapping are intractable to get optimized. We use a genetic algorithm to optimize these parameters. Experimental results showed considerable improvement on the classification accuracy over a crisp discretization and a typical fuzzy discretization.