Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
Combining Divide-and-Conquer and Separate-and-Conquer for Efficient and Effective Rule Induction
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
JSAI'03/JSAI04 Proceedings of the 2003 and 2004 international conference on New frontiers in artificial intelligence
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This paper is concerned with how to classify examples that are not covered by any rule in an unordered hypothesis. Instead of assigning the majority class to the uncovered examples, which is the standard method, a novel method is presented that minimally generalises the rules to include the uncovered examples. The new method, called Rule Stretching, has been evaluated on several domains (using the inductive logic programming system Virtual Predict for induction of the base hypothesis). The results show a significant improvement over the standard method.