The independent choice logic for modelling multiple agents under uncertainty
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Discrete Mathematics and Its Applications
Discrete Mathematics and Its Applications
Approximate Match of Rules Using Backpropagation Neural Networks
Machine Learning
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Propositionalization approaches to relational data mining
Relational Data Mining
Relational data mining applications: an overview
Relational Data Mining
Data Mining and Knowledge Discovery
Attribute-Value Learning Versus Inductive Logic Programming: The Missing Links (Extended Abstract)
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Relational Learning: Hard Problems and Phase Transitions
AI*IA '99 Proceedings of the 6th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
ILP with noise and fixed example size: a Bayesian approach
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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A first-order Bayesian network (FOBN) is an extension of first-order logic in order to cope with uncertainty problems. Therefore, learning an FOBN might be a good idea to build an effective classifier. However, because of a complication of the FOBN, directly learning it from relational data is difficult. This paper proposes another way to learn FOBN classifiers. We adapt Inductive Logic Programming (ILP) and a Bayesian network learner to construct the FOBN. To do this, we propose a feature extraction algorithm to generate the significant parts (features) of ILP rules, and use these features as a main structure of the induced the FOBN. Next, to learn the remaining parts of the FOBN structure and its conditional probability tables by a standard Bayesian network learner, we also propose an efficient propositionalisation algorithm for translating the original data into the single table format. In this work, we provide a preliminary evaluation on the mutagenesis problem, a standard dataset for relational learning problem. The results are compared with the state-of-the-art ILP learner, the PROGOL system.