Making Better Use of Global Discretization

Before applying learning algorithms to datasets, practitioners often globally discretize any numeric attributes. If the algorithm cannot handle numeric attributes directly, prior discretization is essential. Even if it can, prior discretization often accelerates induction, and may produce simpler and more accurate classifiers. As it is generally done, global discretization denies the learning algorithm any chance of taking advantage of the ordering information implicit in numeric attributes. However, a simple transformation of discretized data preserves this information in a form that learners can use. We show that, compared to using the discretized data directly, this transformation significantly increases the accuracy of decision trees built by C4.5, decision lists built by PART, and decision tables built using the wrapper method, on several benchmark datasets. Moreover, it can significantly reduce the size of the resulting classifiers. This simple technique makes global discretizat...

• Type: Conference
• Year: 1999
• Where: ICML
• Authors: Eibe Frank, Ian H. Witten
• Keywords: Global Discretization, Handle Numeric Attributes, ICML 1999, Machine Learning, Numeric Attributes