Applied multivariate statistical analysis
Applied multivariate statistical analysis
Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Adaptive classifiers with ICI-based adaptive knowledge base management
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
An Effective Decentralized Nonparametric Quickest Detection Approach
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Information bounds and quickest change detection in decentralized decision systems
IEEE Transactions on Information Theory
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The prompt detection of faults and, more in general, changes in stationarity in networked systems such as sensor/actuator networks is a key issue to guarantee robustness and adaptability in applications working in real-life environments. Traditional change-detection methods aiming at assessing the stationarity of a data generating process would require a centralized availability of all observations, solution clearly unacceptable when large scale networks are considered and data have local interest. Differently, distributed solutions based on decentralized change-detection tests exploiting information at the unit and cluster level would be a solution. This work suggests a novel distributed change-detection test which operates at two-levels: the first, running on the unit, is particularly reactive in detecting small changes in the process generating the data, whereas the second exploits distributed information at the cluster-level to reduce false positives. Results can be immediately integrated in the machine learning community where adaptive solutions are envisaged to address changes in stationarity of the considered application. A large experimental campaign shows the effectiveness of the approach both on synthetic and real data applications.