C4.5: programs for machine learning
C4.5: programs for machine learning
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Propositionalisation and Aggregates
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Involving Aggregate Functions in Multi-relational Search
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
An Assessment of ILP-Assisted Models for Toxicology and the PTE-3 Experiment
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Probabilistic Relational Models
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Transformation-Based Learning Using Multirelational Aggregation
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Aggregation-based feature invention and relational concept classes
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning relational probability trees
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Improving the efficiency of inductive logic programming through the use of query packs
Journal of Artificial Intelligence Research
Top-down induction of first-order logical decision trees
Artificial Intelligence
Classifying relational data with neural networks
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
ReMauve: A Relational Model Tree Learner
Inductive Logic Programming
Learning Aggregate Functions with Neural Networks Using a Cascade-Correlation Approach
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Complex aggregates in relational learning
AI Communications - Recommender Systems
A Comparison between Neural Network Methods for Learning Aggregate Functions
DS '08 Proceedings of the 11th International Conference on Discovery Science
Type Extension Trees for feature construction and learning in relational domains
Artificial Intelligence
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In relational learning, predictions for an individual are based not only on its own properties but also on the properties of a set of related individuals. Many systems use aggregates to summarize this set. Features thus introduced compare the result of an aggregate function to a threshold. We consider the case where the set to be aggregated is generated by a complex query and present a framework for refining such complex aggregate conditions along three dimensions: the aggregate function, the query used to generate the set, and the threshold value. The proposed aggregate refinement operator allows a more efficient search through the hypothesis space and thus can be beneficial for many relational learners that use aggregates. As an example application, we have implemented the refinement operator in a relational decision tree induction system. Experimental results show a significant efficiency gain in comparison with the use of a less advanced refinement operator.