Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Boolean Feature Discovery in Empirical Learning
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
An Exact Probability Metric for Decision Tree Splitting and Stopping
Machine Learning
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
PROLOG Programming for Artificial Intelligence
PROLOG Programming for Artificial Intelligence
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Oblivious decision trees graphs and top down pruning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Covering vs divide-and-conquer for top-down induction of logic programs
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Rule Induction for Classification of Gene Expression Array Data
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Classifying Uncovered Examples by Rule Stretching
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Concept Induction in Description Logics Using Information-Theoretic Heuristics
International Journal on Semantic Web & Information Systems
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Divide-and-Conquer (DAC) and Separate-and-Conquer (SAC) are two strategies for rule induction that have been used extensively. When searching for rules DAC is maximally conservative w.r.t. decisions made during search for previous rules. This results in a very efficient strategy, which however suffers from diffculties in effectively inducing disjunctive concepts due to the replication problem. SAC on the other hand is maximally liberal in the same respect. This allows for a larger hypothesis space to be searched, which in many cases avoids the replication problem but at the cost of lower effciency. We present a hybrid strategy called Reconsider-and-Conquer (RAC), which handles the replication problem more effectively than DAC by reconsidering some of the earlier decisions and allows for more efficient induction than SAC by holding on to some of the decisions. We present experimental results from propositional, numerical and relational domains demonstrating that RAC significantly reduces the replication problem from which DAC suffers and is several times (up to an order of magnitude) faster than SAC.