C4.5: programs for machine learning
C4.5: programs for machine learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
On the optimization of fuzzy decision trees
Fuzzy Sets and Systems
A Survey of Methods for Scaling Up Inductive Algorithms
Data Mining and Knowledge Discovery
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning Decision Trees Using the Area Under the ROC Curve
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Tree Induction for Probability-Based Ranking
Machine Learning
A complete fuzzy decision tree technique
Fuzzy Sets and Systems - Theme: Learning and modeling
Machine Learning
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Learning probabilistic decision trees for AUC
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
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
Improving the ranking performance of decision trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Designing decision trees with the use of fuzzy granulation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An Empirical Comparison of Probability Estimation Techniques for Probabilistic Rules
DS '09 Proceedings of the 12th International Conference on Discovery Science
Learning interpretable fuzzy inference systems with FisPro
Information Sciences: an International Journal
Fuzzy machine learning and data mininga
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
A systematic fuzzy rule based approach for fault classification in transmission lines
Applied Soft Computing
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Several fuzzy extensions of decision tree induction, which is an established machine-learning method, have already been proposed in the literature. So far, however, fuzzy decision trees have almost exclusively been used for the performance task of classification. In this paper, we show that a fuzzy extension of decision trees is arguably more useful for another performance task, namely ranking. Roughly, the goal of ranking is to order a set of instances from most likely positive to most likely negative. The motivation for applying fuzzy decision trees to this problem originates from recent investigations of the ranking performance of conventional decision trees. These investigations will be continued and complemented in this paper. Our results reveal some properties that seem to be crucial for a good ranking performance--properties that are better and more naturally offered by fuzzy than by conventional decision trees. Most notably, a fuzzy decision tree produces scores in terms of membership degrees on a fine-granular scale. Using these membership degrees as a ranking criterion, a key problem of conventional decision trees is solved in an elegant way, namely the question of how to break ties between instances in the same leaf or, more generally, between equally scored instances.