Pruning Algorithms for Rule Learning
Machine Learning
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Tree Induction for Probability-Based Ranking
Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Why fuzzy decision trees are good rankers
IEEE Transactions on Fuzzy Systems
Improving the ranking performance of decision trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
Hi-index | 0.00 |
Rule learning is known for its descriptive and therefore comprehensible classification models which also yield good class predictions. However, in some application areas, we also need good class probability estimates. For different classification models, such as decision trees, a variety of techniques for obtaining good probability estimates have been proposed and evaluated. However, so far, there has been no systematic empirical study of how these techniques can be adapted to probabilistic rules and how these methods affect the probability-based rankings. In this paper we apply several basic methods for the estimation of class membership probabilities to classification rules. We also study the effect of a shrinkage technique for merging the probability estimates of rules with those of their generalizations.