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
The Strength of Weak Learnability
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Further experimental evidence against the utility of Occam's razor
Journal of Artificial Intelligence Research
The Role of Occam‘s Razor in Knowledge Discovery
Data Mining and Knowledge Discovery
Controlled Flux Results in Stable Decision Trees
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Decision tree grafting from the all-tests-but-one partition
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
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This paper extends recent work on decision tree grafting. Grafting is an inductive process that adds nodes to inferred decision trees. This process is demonstrated to frequently improve predictive accuracy. Superficial analysis might suggest that decision tree grafting is the direct reverse of pruning. To the contrary, it is argued that the two processes are complementary. This is because, like standard tree growing techniques, pruning uses only local information, whereas grafting uses non-local information. The use of both pruning and grafting in conjunction is demonstrated to provide the best general predictive accuracy over a representative selection of learning tasks.