Publication details

Handling Time Changing Data with Adaptive Very Fast Decision Rules



Year of publication 2012
Type Article in Proceedings
Conference Machine Learning and Knowledge Discovery in Databases ECML/PKDD
MU Faculty or unit

Faculty of Informatics

Field Informatics
Keywords Data Streams; Decision Rules; Concept Drift
Description Data streams are usually characterized by changes in the underlying distribution generating data. Therefore algorithms designed to work with data streams should be able to detect changes and quickly adapt the decision model. Rules are one of the most interpretable and flexible models for data mining prediction tasks. In this paper we present the Adaptive Very Fast Decision Rules (AVFDR), an on-line, any-time and one-pass algorithm for learning decision rules in the context of time changing data. AVFDR can learn ordered and unordered rule sets. It is able to adapt the decision model via incremental induction and specialization of rules. Detecting local drifts takes advantage of the modularity of rule sets. In AVFDR, each individual rule monitors the evolution of performance metrics to detect concept drift. AVFDR prunes rules that detect drift. This explicit change detection mechanism provides useful information about the dynamics of the process generating data, faster adaption to changes and generates compact rule sets. The experimental evaluation shows this method is able to learn fast and compact rule sets from evolving streams in comparison to alternative methods.
Related projects:

You are running an old browser version. We recommend updating your browser to its latest version.

More info