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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Discretizing Continuous Attributes Using Information Theory
verfasst von : Chang-Hwan Lee
Erschienen in: Computer and Information Sciences - ISCIS 2005
Verlag: Springer Berlin Heidelberg
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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. 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.