Dealing with structured data needs the use of expressive representation formalisms that, however, puts the problem to deal with the computational complexity of the machine learning process. Furthermore, real world domains require tools able to manage their typical uncertainty. Many statistical relational learning approaches try to deal with these problems by combining the construction of relevant relational features with a probabilistic tool. When the combination is static (static propositionalization), the constructed features are considered as boolean features and used offline as input to a statistical learner; while, when the combination is dynamic (dynamic propositionalization), the feature construction and probabilistic tool are combined into a single process. In this paper we propose a selective propositionalization method that search the optimal set of relational features to be used by a probabilistic learner in order to minimize a loss function. The new propositionalization approach has been combined with the random subspace ensemble method. Experiments on real-world datasets shows the validity of the proposed method.
Since, the effectiveness of learning algorithms strongly depends on the used features, a feature selection task is very desirable. The aim of feature selection is to find an optimal subset of the input features leading to high classification performance, or, more generally, to carry out the classification task optimally. However, the search for a variable subset is a NP-hard problem. Therefore, the optimal solution cannot be guaranteed to be reached except when performing an exhaustive search in the solution space. Using stochastic local search procedures @cite_7 allows one to obtain good solutions without having to explore the whole solution space.
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Prologue Part I. Foundations 1. Introduction 2. SLS Methods 3. Generalised Local Search Machines 4. Empirical Analysis of SLS Algorithms 5. Search Space Structure and SLS Performance Part II. Applications 6. SAT and Constraint Satisfaction 7. MAX-SAT and MAX-CSP 8. Travelling Salesman Problems 9. Scheduling Problems 10. Other Combinatorial Problems Epilogue Glossary