Non-Disjoint Discretization for Naive-Bayes Classifiers

Previous discretization techniques have discretized numeric attributes into disjoint intervals. We argue that this is neither necessary nor appropriate for naive-Bayes classifiers. The analysis leads to a new discretization method, Non-Disjoint Discretization (NDD). NDD forms overlapping intervals for a numeric attribute, always locating a value toward the middle of an interval to obtain more reliable probability estimation. It also adjusts the number and size of discretized intervals to the number of training instances, seeking an appropriate trade-off between bias and variance of probability estimation. We justify NDD in theory and test it on a wide cross-section of datasets. Our experimental results suggest that for naiveBayes classifiers, NDD works better than alternative discretization approaches.

• Type: Conference
• Year: 2002
• Where: ICML
• Authors: Ying Yang, Geoffrey I. Webb
• Keywords: Alternative Discretization Approaches, ICML 2002, Machine Learning, NDD Forms, Non-Disjoint Discretization