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
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This paper examines some initialization methods for a genetic based machine learning (GBML) rule representation [2] which works with adaptive discretization intervals. The methods studied apply different degrees of uniformness to the initial intervals of the population. The tests done show that except the test problems with more attributes, the differences between the tested methods accuracies are not significant. This proves that we only have to be aware of it in a limited kind of problems.