Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
The weighted majority algorithm
Information and Computation
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Diversity in Combinations of Heterogeneous Classifiers
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Boosting k-nearest neighbor classifier by means of input space projection
Expert Systems with Applications: An International Journal
ACM Computing Surveys (CSUR)
Redundancy and its applications in wireless sensor networks: a survey
WSEAS Transactions on Computers
Comparison of Bagging and Boosting Algorithms on Sample and Feature Weighting
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Ensemble with neural networks for bankruptcy prediction
Expert Systems with Applications: An International Journal
Spatiotemporal anomaly detection in gas monitoring sensor networks
EWSN'08 Proceedings of the 5th European conference on Wireless sensor networks
A comparative assessment of ensemble learning for credit scoring
Expert Systems with Applications: An International Journal
Fuzzy modeling using generalized neural networks and Kalman filter algorithm
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Detection of process anomalies using an improved statistical learning framework
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
IEEE Transactions on Signal Processing
Engineering Applications of Artificial Intelligence
A survey of multiple classifier systems as hybrid systems
Information Fusion
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Wireless sensor networks are often used to monitor and measure physical characteristics from remote and sometimes hostile environments. In these circumstances the sensing data accuracy is a crucial attribute for the way these applications complete their objectives, requiring efficient solutions to discover sensor anomalies. Such solutions are hard to be found mainly because the intricate defining of the correct sensor behavior in a complex and dynamic environment. This paper tackles the sensing anomaly detection from a new perspective by modeling the correct operation of sensors not by one, but by five different dynamical models, acting synergically to provide a reliable solution. Our methodology relies on an ensemble based system composed of a set of diverse binary classifiers, adequately selected, to implement a complex decisional system on network base station. Moreover, every time a sensing anomaly is discovered, our ensemble offers a reliable estimation to replace the erroneous measurement provided by sensor.