Active Relational Rule Learning in a Constrained Confidence-Rated Boosting Framework

In this dissertation, I investigate the poten\tial of boosting within the framework of relational rule learning. Boosting is a particularly robust and powerful technique to enhance the prediction accuracy of systems that learn from examples. Although boosting has been extensively studied in the last years for propositional learning systems, only little attention has been paid to boosting in relational learning. In this work, I identify the particular challenges brought about by boosting relational rule learners. I show that boosting can be accomplished successfully for relational rule learning, where success is defined in terms of prediction accuracy, learning time complexity and the interpretability of learning results. To this end, I propose C2RIB, an efficient, effective and usable boosted ILP learner which is based on a boosting framework and a relational rule learner, which together account for learning time complexity and understandability of learning results as the two specific challenges we are confronted with when boosting relational rule learners. A thorough empirical evaluation of C2RIB indicates that this particular boosted relational rule learner performs competitively with state-of-the-art ILP systems with respect to learning time, predictive accuracy, and interpretability of learning results. Furthermore, I investigate the potential of boosting for active relational feature selection. Feature selection, the search for a subset of most relevant features among the available ones, constitutes an important technique for systems that learn from examples to improve their efficiency and enhance the quality and interpretability of their results. Most feature selection methods in relational learning transform the given examples from a relational into a propositional representation in order to utilise a propositional feature selection algorithm. Moreover, existing approaches are passive in so far as they determine feature subsets to be used for learning prior to the actual learning process. In contrast, here I propose C2RIBD, a learning system that successfully embeds active feature selection into a boosted ILP learner, and selects relevant features on the basis of the current learning process without performing a change of representation. C2RIBD yields significant reductions of learning time complexity, while maintaining both the predictive accuracy and the intelligibility of the hypotheses that are learned. Keywords: Machine Learning; Inductive Logic Programming; constrained cofidence-rated ILP boosting; embedded active relational feature selection