An Empirical Comparison of Discretization Methods

Many machine learning and neurally inspired algorithms are limited, at least in their pure form, to working with nominal data. However, for many real-world problems, some provision must be made to support processing of continuously valued data. This paper presents empirical results obtained by using six different discretization methods as preprocessors to three different supervised learners on several real-world problems. No discretization technique clearly outperforms the others. Also, discretization as a preprocessing step is in many cases found to be inferior to direct handling of continuously valued data. These results suggest that machine learning algorithms should be designed to directly handle continuously valued data rather than relying on preprocessing or ad hoc techniques.