First order random forests with complex aggregates

Random forest induction is a bagging method that randomly samples the feature set at each node in a decision tree. In propositional learning, the method has been shown to work well when lots of features are available. This certainly is the case in first order learning, especially when aggregate functions, combined with selection conditions on the set to be aggregated, are included in the feature space. In this paper, we introduce a random forest based approach to learning first order theories with aggregates. We experimentally validate and compare several variants: first order random forests without aggregates, with simple aggregates, and with complex aggregates in the feature set.status: publishe

First order random forests: Learning relational classifiers with complex aggregates

In relational learning, predictions for an individual are based not only on its own properties but also on the properties of a set of related individuals. Relational classifiers differ with respect to how they handle these sets: some use properties of the set as a whole (using aggregation), some refer to properties of specific individuals of the set, however, most classifiers do not combine both. This imposes an undesirable bias on these learners. This article describes a learning approach that avoids this bias, using first order random forests. Essentially, an ensemble of decision trees is constructed in which tests are first order logic queries. These queries may contain aggregate functions, the argument of which may again be a first order logic query. The introduction of aggregate functions in first order logic, as well as upgrading the forest's uniform feature sampling procedure to the space of first order logic, generates a number of complications. We address these and propose a solution for them. The resulting first order random forest induction algorithm has been implemented and integrated in the ACE-ilProlog system, and experimentally evaluated on a variety of datasets. The results indicate that first order random forests with complex aggregates are an efficient and effective approach towards learning relational classifiers that involve aggregates over complex selections.status: publishe

First order random forests: Learning relational classifiers with complex aggregates

Abstract. Given the widespread use of relational databases, it is worthwhile to study the behaviour of relational learners in this context. Relational classifiers differ with respect to how they handle sets of related tuples: some use properties of the set as a whole (using aggregation), some refer to properties of specific elements of the set, however, most classifiers do not combine both. This imposes an undesirable bias on these learners. This article describes a learning approach that avoids this bias, using of first order random forests. Essentially, an ensemble of decision trees is constructed in which tests are first order logic queries. These queries may contain aggregate functions, the argument of which may again be a first order logic query. The introduction of aggregate functions in first order logic, as well as upgrading the forest’s uniform feature sampling procedure to the space of first order logic, generate a number of complications. We address these and propose a solution for them. The resulting first order random forest induction algorithm has been implemented and integrated in the ACE-ilProlog system, and experimentally evaluated on a variety of datasets. The results indicate that first order random forests with complex aggregates are an efficient and effective approach towards learning relational classifiers that involve aggregates over complex selections

First Order Random Forests:

In relational learning, predictions for an individual are based not only on its own properties but also on the properties of a set of related individuals. Relational classifiers di#er with respect to how they handle these sets: some use properties of the set as a whole (using aggregation), some refer to properties of specific individuals of the set, however, most classifiers do not combine both. This imposes an undesirable bias on these learners. This article describes a learning approach that avoids this bias, using first order random forests. Essentially, an ensemble of decision trees is constructed in which tests are first order logic queries. These queries may contain aggregate functions, the argument of which may again be a first order logic query. The introduction of aggregate functions in first order logic, as well as upgrading the forest's uniform feature sampling procedure to the space of first order logic, generates a number of complications. We address these and propose a solution for them. The resulting first order random forest induction algorithm has been implemented and integrated in the ACE-ilProlog system, and experimentally evaluated on a variety of datasets. The results indicate that first order random forests with complex aggregates are an e#cient and e#ective approach towards learning relational classifiers that involve aggregates over complex selections