Improving Classification Accuracy With Discretization On Datasets Including Continuous Valued Features
Mehmet Hacıbeyoğlu, Ahmet Hamdi Arslan, Şirzat Kahramanlı · Zenodo (CERN European Organization for Nuclear Research) · 2011
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%.