C4.5: programs for machine learning
C4.5: programs for machine learning
Making large-scale support vector machine learning practical
Advances in kernel methods
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Inductive logic programming for knowedge discovery in databases
Relational Data Mining
A Relevancy Filter for Constructive Induction
IEEE Intelligent Systems
A Framework for the Investigation of Aggregate Functions in Database Queries
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Estimating the Generalization Performance of an SVM Efficiently
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Stochastic Propositionalization of Non-determinate Background Knowledge
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
An extended transformation approach to Inductive Logic Programming
An extended transformation approach to Inductive Logic Programming
Scaling Boosting by Margin-Based Inclusionof Features and Relations
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Spatial Subgroup Mining Integrated in an Object-Relational Spatial Database
PKDD '02 Proceedings of the 6th 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
Feature Selection for Propositionalization
DS '02 Proceedings of the 5th International Conference on Discovery Science
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
The Study of Dynamic Aggregation of Relational Attributes on Relational Data Mining
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Learning Aggregate Functions with Neural Networks Using a Cascade-Correlation Approach
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
A Comparison between Neural Network Methods for Learning Aggregate Functions
DS '08 Proceedings of the 11th International Conference on Discovery Science
Discovering Knowledge from Multi-relational Data Based on Information Retrieval Theory
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Refining aggregate conditions in relational learning
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Classifying relational data with neural networks
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Inductive databases in the relational model: the data as the bridge
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Subgroup discovery using bump hunting on multi-relational histograms
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
Type Extension Trees for feature construction and learning in relational domains
Artificial Intelligence
| Hi-index | 0.01 |
Given the very widespread use of multirelational databases, ILP systems are increasingly being used on data originating from such warehouses. Unfortunately, even though not complex in structure, such business data often contain highly non-determinate components, making them difficult for ILP learners geared towards structurally complex tasks. In this paper, we build on popular transformation-based approaches to ILP and describe how they can naturally be extended with relational aggregation. We experimentall y show that this results in a multirelational learner that outperforms a structurally-oriented ILP system both in speed and accuracy on this class of problems.