A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
General and Efficient Multisplitting of Numerical Attributes
Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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
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
Evaluating Feature Selection Methods for Learning in Data Mining Applications
HICSS '98 Proceedings of the Thirty-First Annual Hawaii International Conference on System Sciences-Volume 5 - Volume 5
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Genetic fuzzy discretization with adaptive intervals for classification problems
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
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
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We propose a new genetic fuzzy discretization method with feature selection for the pattern classification problems. Traditional discretization methods categorize a continuous attribute 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 and a discretization process increases the total amount of data being transformed. We use a genetic algorithm with feature selection not only to optimize these parameters but also to reduce the amount of transformed data by filtering the unconcerned attributes. Experimental results showed considerable improvement on the classification accuracy over a crisp discretization and a typical fuzzy discretization with feature selection.