Learning decision rules in noisy domains
Proceedings of Expert Systems '86, The 6Th Annual Technical Conference on Research and development in expert systems III
International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Inferring decision trees using the minimum description length principle
Information and Computation
A Comparative Analysis of Methods for Pruning Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Comparisons in Induction Algorithms
Machine Learning
Machine Learning
A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
From Ensemble Methods to Comprehensible Models
DS '02 Proceedings of the 5th International Conference on Discovery Science
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
A Case-Based Explanation System for Black-Box Systems
Artificial Intelligence Review
A Comparison of Decision Tree Ensemble Creation Techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Statistics & Data Analysis
A Non-sequential Representation of Sequential Data for Churn Prediction
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
Kernel combination versus classifier combination
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
K nearest sequence method and its application to churn prediction
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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A generalisation of bottom-up pruning is proposed as a model level combination method for a decision tree ensemble. Bottom up pruning on a single tree involves choosing between a subtree rooted at a node, and a leaf, dependant on a pruning criterion. A natural extension to an ensemble of trees is to allow subtrees from other ensemble trees to be grafted onto a node in addition to the operations of pruning to a leaf and leaving the existing subtree intact. Suitable pruning criteria are proposed and tested for this multi-tree pruning context. Gains in both performance and in particular compactness over individually pruned trees are observed in tests performed on a number of datasets from the UCI database. The method is further illustrated on a churn prediction problem in the telecommunications domain.