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Dynamic Discretization of Continuous Attributes by João Gama; Luis Torgo; Carlos Soares is a book available to read on EtoBox.

What is Dynamic Discretization of Continuous Attributes about?

Discretization of continuous attributes is an important task for certain types of machine learning algorithms. Bayesian approaches, for instance, require assumptions about data distributions. Decision Trees, on the other hand, require sorting operations to deal with continuous attributes, which largely increase learning times. This paper presents a new method of discretization, whose main characteristic is that it takes into account interdependencies between attributes. Detecting interdependencies can be seen as discovering redundant attributes. This means that our method performs attribute selection as a side effect of the discretization. Empirical evaluation on five benchmark datasets from UCI repository, using C4.5 and a naive Bayes, shows a consistent reduction of the features without loss of generalization accuracy.

Author

João Gama; Luis Torgo; Carlos Soares

Published

1998

Language

EN