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.
nFOIL @cite_1 and kFOIL @cite_17 are two examples of dynamic propositionalization. Differently from the static propositionalization, where firstly the features have been generated and then the parameters for a statistical learner are estimated, they tightly integrates the learning of the features with the statistical propositional learner. The criterion according to which the features are generated is that of a statistical learner, a na " i ve Bayes in the case of nFOIL and a support vector machine (SVM) for kFOIL . Both the methods employ an adaptation of the well-known FOIL algorithm @cite_11 that implements a separate-and-conquer rule learning algorithm.
@cite_11 @cite_1 @cite_17
1999138184 1608154539
This paper describes FOIL, a system that learns Horn clauses from data expressed as relations. FOIL is based on ideas that have proved effective in attribute-value learning systems, but extends them to a first-order formalism. This new system has been applied successfully to several tasks taken from the machine learning literature. A novel and simple combination of inductive logic programming with kernel methods is presented. The kFOIL algorithm integrates the well-known inductive logic programming system FOIL with kernel methods. The feature space is constructed by leveraging FOIL search for a set of relevant clauses. The search is driven by the performance obtained by a support vector machine based on the resulting kernel. In this way, kFOIL implements a dynamic propositionalization approach. Both classification and regression tasks can be naturally handled. Experiments in applying kFOIL to well-known benchmarks in chemoinformatics show the promise of the approach.