Paper Info
Title
Sensor event mining with hybrid ensemble learning and evolutionary feature subset selection model
Abstract
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.
Year
DOI
Venue
2015
10.1109/BigData.2015.7364001
Big Data
Keywords
Field
DocType
multi-objective evolutionary algorithm, feature subset selection, rule induction, complex event processing, ensemble learning
Data mining,Evolutionary algorithm,Computer science,Evolutionary computation,Complex event processing,Artificial intelligence,Rule induction,Statistical classification,Ensemble learning,Genetic algorithm,Machine learning,Fuzzy rule
Conference
Citations 
PageRank 
References 
1
0.35
26
Authors
4
Name
Order
Citations
PageRank
Nijat Mehdiyev1577.75
Julian Krumeich28810.73
dirk werth320242.72
Peter Loos447940.84