Maximizing the predictive value of production rules
Artificial Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
A Modified Chi2 Algorithm for Discretization
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
On Changing Continuous Attributes into Ordered Discrete Attributes
EWSL '91 Proceedings of the European Working Session on Machine Learning
On the best finite set of linear observables for discriminating two Gaussian signals
IEEE Transactions on Information Theory
Selection and optimization of cut-points for numeric attribute values
Computers & Mathematics with Applications
Analysis of the Effectiveness of the Genetic Algorithms based on Extraction of Association Rules
Fundamenta Informaticae - Intelligent Data Analysis in Granular Computing
Supporting scalable Bayesian networks using configurable discretizer actuators
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
The Knowledge Engineering Review
QAR-CIP-NSGA-II: A new multi-objective evolutionary algorithm to mine quantitative association rules
Information Sciences: an International Journal
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Many classification algorithms require that training examples contain only discrete values. In order to use these algorithms when some attributes have continuous numeric values, the numeric attributes must be converted into discrete ones. This paper describes a new way of discretizing numeric values using information theory. Our method is context-sensitive in the sense that it takes into account the value of the target attribute. The amount of information each interval gives to the target attribute is measured using Hellinger divergence, and the interval boundaries are decided so that each interval contains as equal amount of information as possible. In order to compare our discretization method with some current discretization methods, several popular classification data sets are selected for discretization. We use naive Bayesian classifier and C4.5 as classification tools to compare the accuracy of our discretization method with that of other methods.