Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
An Evolutionary Algorithm Using Multivariate Discretization for Decision Rule Induction
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Evolution of Multi-adaptive Discretization Intervals for a Rule-Based Genetic Learning System
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Evolution Of Adaptive Discretization Intervals For A Rule-based Genetic Learning System
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Search-intensive concept induction
Evolutionary Computation
A mixed discrete-continuous attribute list representation for large scale classification domains
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Development of the Data Preprocessing Agent's Knowledge for Data Mining Using Rough Set Theory
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Handling continuous-valued attributes in incremental first-order rules learning
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
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This paper proposes a method for dealing with numerical attributes in inductive concept learning systems based on genetic algorithms. The method uses constraints for restricting the range of values of the attributes and novel stochastic operators for modifying the constraints. These operators exploit information on the distribution of the values of an attribute. The method is embedded into a GA based system for inductive logic programming. Results of experiments on various data sets indicate that the method provides an effective local discretization tool for GA based inductive concept learners.