C4.5: programs for machine learning
C4.5: programs for machine learning
A new version of the rule induction system LERS
Fundamenta Informaticae
Data reduction: discretization of numerical attributes
Handbook of data mining and knowledge discovery
Cluster Analysis
A comparison of six approaches to discretization: a rough set perspective
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Dynamic discreduction using Rough Sets
Applied Soft Computing
A fuzzy-rough sets based compact rule induction method for classifying hybrid data
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Compact classification of optimized Boolean reasoning with Particle Swarm Optimization
Intelligent Data Analysis
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We present results of extensive experiments performed on nine data sets with numerical attributes using six promising discretization methods. For every method and every data set 30 experiments of ten-fold cross validation were conducted and then means and sample standard deviations were computed. Our results show that for a specific data set it is essential to choose an appropriate discretization method since performance of discretization methods differ significantly. However, in general, among all of these discretization methods there is no statistically significant worst or best method. Thus, in practice, for a given data set the best discretization method should be selected individually.