New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Communications of the ACM
Term Indexing
Associative-Commutative Discrimination Nets
TAPSOFT '93 Proceedings of the International Joint Conference CAAP/FASE on Theory and Practice of Software Development
Mining Association Rules in Multiple Relations
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Which Hypotheses Can Be Found with Inverse Entailment?
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Query transformations for improving the efficiency of ilp systems
The Journal of Machine Learning Research
Improving the efficiency of inductive logic programming through the use of query packs
Journal of Artificial Intelligence Research
Faster association rules for multiple relations
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
April: an inductive logic programming system
JELIA'06 Proceedings of the 10th European conference on Logics in Artificial Intelligence
Strategies to parallelize ILP systems
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
k-RNN: k-relational nearest neighbour algorithm
Proceedings of the 2008 ACM symposium on Applied computing
Compile the Hypothesis Space: Do it Once, Use it Often
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Conceptual clustering of multi-relational data
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Compile the Hypothesis Space: Do it Once, Use it Often
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
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Despite the considerable success of Inductive Logic Programming (ILP), deployed ILP systems still have efficiency problems when applied to complex problems. Several techniques have been proposed to address the efficiency issue. Such proposals include query transformations, query packs, lazy evaluation and parallel execution of ILP systems, to mention just a few. We propose a novel technique that avoids the procedure of deducing each example to evaluate each constructed clause. The technique takes advantage of the two stage procedure of Mode Directed Inverse Entailment (MDIE) systems. In the first stage of a MDIE system, where the bottom clause is constructed, we store not only the bottom clause but also valuable additional information. The information stored is sufficient to evaluate the clauses constructed in the second stage without the need for a theorem prover. We used a data structure called Trie to efficiently store all bottom clauses produced using all examples (positive and negative) as seeds. The technique was implemented and evaluated using two well known data sets from the ILP literature. The results are promising both in terms of execution time and accuracy.