Ensembles of decision rules

In most approaches to ensemble methods, base classifiers are decision trees or decision stumps. In this paper, we consider an algorithm that generates an ensemble of decision rules that are simple classifiers in the form of logical expression: if [conditions], then [decision]. Single decision rule indicates only one of the decision classes. If an object satisfies conditions of the rule, then it is assigned to that class. Otherwise the object remains unassigned. Decision rules were common in the early machine learning approaches. The most popular decision rule induction algorithms were based on sequential covering procedure. The algorithm presented here follows a different approach to decision rule generation. It treats a single rule as a subsidiary, base classifier in the ensemble. First experimental results have shown that the presented algorithm is competitive with other methods. Additionally, generated decision rules are easy in interpretation, which is not the case of other types of base classifiers.