Sensor Event Mining with Hybrid Ensemble Learning and Evolutionary Feature Subset Selection Model

Nijat Mehdiyev, Julian Krumeich, Dirk Werth, Peter Loos

In: Proceedings of IEEE International Conference on Big Data (Big Data) 2015. IEEE Conference on Big Data October 29-November 1 Santa Clara CA United States Seiten 2159-2168 IEEE 10/2015.


Recent advancements in sensor technology offer opportunities to manage business processes in a proactive manner. To enable an effective and real-time monitoring, sensor data have to be treated and processed in an event processing manner. Complex Event Processing is an efficient technology that detects useful complex events by matching primitive sensor events using event patterns. Event patterns can be represented as templates that combine primitive events by temporal, logical, spatial and sequential correlations to detect more complex events. Identifying event patterns out of streaming data with a high data volume and velocity is a challenging task. In this paper, we propose an Ensemble Model consisting of a crisp and fuzzy rule based classifiers in order to derive decision rules as event patterns. Before implementing the ensemble classifier directly to the streaming data, we select the most influential feature subset using a multi-objective evolutionary algorithm. The performance of the proposed model was evaluated using real data obtained from accelerometer sensors. Promising results with high accuracy and appropriate level of computational complexity were obtained and discussed.


Weitere Links

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence