Propositionalization approaches to relational data mining
Relational Data Mining
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Feature Construction with Version Spaces for Biochemical Applications
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Multi-Dimensional Relational Sequence Mining
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Journal of Artificial Intelligence Research
Probabilistic inductive logic programming
TildeCRF: conditional random fields for logical sequences
ECML'06 Proceedings of the 17th European conference on Machine Learning
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This paper tackles the problem of relational sequence learning selecting relevant features elicited from a set of labelled sequences. Each relational sequence is firstly mapped into a feature vector using the result of a feature construction method. The second step finds an optimal subset of the constructed features that leads to high classification accuracy, by adopting a wrapper approach that uses a stochastic local search algorithm embedding a Bayes classifier. The performance of the proposed method on a real-world dataset shows an improvement compared to other sequential statistical relational methods, such as Logical Hidden Markov Models and relational Conditional Random Fields.