This paper describes a new rule induction system, rila, which can extract frequent patterns from multiple connected relations. The system supports two different rule selection strategies, namely the select early and select late strategies. Pruning heuristics are used to control the number of hypotheses generated during the learning process. Experimental results are provided on the mutagenesis and the segmentation data sets. The present rule induction algorithm is also compared to the similar relational learning algorithms. Results show that the algorithm is comparable to similar algorithms.
- Relational rule induction
- Rule selection strategies
ASJC Scopus subject areas
- Management Information Systems
- Information Systems and Management
- Artificial Intelligence