C4.5: programs for machine learning
C4.5: programs for machine learning
The power of sampling in knowledge discovery
PODS '94 Proceedings of the thirteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Machine Learning
Peepholing: Choosing Attributes Efficiently for Megainduction
ML '92 Proceedings of the Ninth International Workshop on Machine Learning
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Scaling Up Inductive Logic Programming by Learning from Interpretations
Data Mining and Knowledge Discovery
Journal of Artificial Intelligence Research
A study of the effect of different types of noise on the precision of supervised learning techniques
Artificial Intelligence Review
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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Windowing has been proposed as a procedure for efficient memory use in the ID3 decision tree learning algorithm. However, it was shown that it may often lead to a decrease in performance, in particular in noisy domains. Following up on previous work, where we have demonstrated that the ability of rule learning algorithms to learn rules independently can be exploited for more efficient windowing procedures, we demonstrate in this paper how this property can be exploited to achieve noisetolerance in windowing.