Download JAR files tagged by merit with all dependencies

This package provides two classes - one for evaluating the merit of individual attributes using a classifier (ClassifierAttributeEval), and second for evaluating the merit of subsets of attributes using a classifier (ClassifierSubsetEval). Both invoke a user-specified classifier to perform the evaluation, either under cross-validation or on the training data.

Class for building and using a decision table/naive bayes hybrid classifier. At each point in the search, the algorithm evaluates the merit of dividing the attributes into two disjoint subsets: one for the decision table, the other for naive Bayes. A forward selection search is used, where at each step, selected attributes are modeled by naive Bayes and the remainder by the decision table, and all attributes are modelled by the decision table initially. At each step, the algorithm also considers dropping an attribute entirely from the model. For more information, see: Mark Hall, Eibe Frank: Combining Naive Bayes and Decision Tables. In: Proceedings of the 21st Florida Artificial Intelligence Society Conference (FLAIRS), 318-319, 2008.