RILL: Algorithm for learning rules from streaming data with concept drift

Abstract

Incremental learning of classification rules from data streams with concept drift is considered. We introduce a new algorithm RILL, which induces rules and single instances, uses bottom-up rule generalization based on nearest rules, and performs intensive pruning of the obtained rule set. Its experimental evaluation shows that it achieves better classification accuracy and memory usage than the related rule algorithm VFDR and it is also competitive to decision trees VFDT-NB. © 2014 Springer International Publishing.