Abstract: This study analyzes the effect of discretization on
classification of datasets including continuous valued features. Six
datasets from UCI which containing continuous valued features are
discretized with entropy-based discretization method. The
performance improvement between the dataset with original features
and the dataset with discretized features is compared with k-nearest
neighbors, Naive Bayes, C4.5 and CN2 data mining classification
algorithms. As the result the classification accuracies of the six
datasets are improved averagely by 1.71% to 12.31%.