Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Evolution Of Adaptive Discretization Intervals For A Rule-based Genetic Learning System
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Chi2: Feature Selection and Discretization of Numeric Attributes
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
Effects of code growth and parsimony pressure on populations in genetic programming
Evolutionary Computation
Flexible learning of problem solving heuristics through adaptive search
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
Learning concept classification rules using genetic algorithms
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
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
Nonuniform dynamic discretization in hybrid networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
A method for handling numerical attributes in GA-based inductive concept learners
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
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Genetic Based Machine Learning (GBML) systems traditionally have evolved rules that only deal with discrete attributes. Therefore, some discretization process is needed in order to teal with real-valued attributes.There are several methods to discretize real-valued attributes into a finite number of intervals, however none of them can efficiently solve all the possible problems. The alternative of a high number of simple uniform-width intervals usually expands the size of the search space without a clear performance gain. This paper proposes a rule representation which uses adaptive discrete intervals that split or merge through the evolution process, finding the correct discretization intervals at the same time as the learning process is done.