Comparing discretization and selection methods for the logical-combinatorial classification of continuous parameters

Preprocessing methods for handling problems with features containing continuous attributes are discussed for learning a classification algorithm based on the JSM method. Discretization methods for continuous parameters that do not make use of class information on feature distribution are compared to entropy-based methods employing class labels in interval partitioning. An entropy-information-based method for selecting attributes is also discussed.