Algorithms from P to NP (vol. 1): design and efficiency
Algorithms from P to NP (vol. 1): design and efficiency
On changing continuous attributes into ordered discrete attributes
EWSL-91 Proceedings of the European working session on learning on Machine learning
Class-Driven Statistical Discretization of Continuous Attributes (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Search-Based Class Discretization
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Data Mining and Knowledge Discovery
International Journal of Approximate Reasoning
Core-generating discretization for rough set feature selection
Transactions on rough sets XIII
Discretization of multidimensional web data for informative dense regions discovery
CIS'04 Proceedings of the First international conference on Computational and Information Science
Model trees for classification of hybrid data types
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
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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.