Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
A mathematical theory of communication
ACM SIGMOBILE Mobile Computing and Communications Review
Fault management in event-driven wireless sensor networks
MSWiM '04 Proceedings of the 7th ACM international symposium on Modeling, analysis and simulation of wireless and mobile systems
Scalable design of fault-tolerance for wireless sensor networks
Scalable design of fault-tolerance for wireless sensor networks
On Distributed Fault-Tolerant Detection in Wireless Sensor Networks
IEEE Transactions on Computers
Distributed fault detection of wireless sensor networks
DIWANS '06 Proceedings of the 2006 workshop on Dependability issues in wireless ad hoc networks and sensor networks
Neuro-Fuzzy Model-Based CUSUM Method Application in Fault Detection on an Autonomous Vehicle
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
Distributed Bayesian fault diagnosis of jump Markov systems in wireless sensor networks
International Journal of Sensor Networks
Multiple feature fusion by subspace learning
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Wearable Activity Tracking in Car Manufacturing
IEEE Pervasive Computing
Dealing with sensor displacement in motion-based onbody activity recognition systems
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Computing Correlation Anomaly Scores Using Stochastic Nearest Neighbors
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
ACM Computing Surveys (CSUR)
Adaptive sensor fault detection and identification using particle filter algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Generalized likelihood ratio tests for change detection in diffusion tensor images
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Towards incremental classifier fusion
Intelligent Data Analysis
Parameter optimization of Kernel-based one-class classifier on imbalance text learning
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
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IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
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LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Detecting and Rectifying Anomalies in Body Sensor Networks
BSN '11 Proceedings of the 2011 International Conference on Body Sensor Networks
Distributed anomaly detection using 1-class SVM for vertically partitioned data
Statistical Analysis and Data Mining
Iterative Generalized-Likelihood Ratio Test for MIMO Radar
IEEE Transactions on Signal Processing
Sensing Assessment in Unknown Environments: A Survey
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Distributed fault-tolerant classification in wireless sensor networks
IEEE Journal on Selected Areas in Communications
Just-in-Time Adaptive Classifiers—Part I: Detecting Nonstationary Changes
IEEE Transactions on Neural Networks
Personal and Ubiquitous Computing
The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition
Pattern Recognition Letters
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Detection of anomalies is a broad field of study, which is applied in different areas such as data monitoring, navigation, and pattern recognition. In this paper we propose two measures to detect anomalous behaviors in an ensemble of classifiers by monitoring their decisions; one based on Mahalanobis distance and another based on information theory. These approaches are useful when an ensemble of classifiers is used and a decision is made by ordinary classifier fusion methods, while each classifier is devoted to monitor part of the environment. Upon detection of anomalous classifiers we propose a strategy that attempts to minimize adverse effects of faulty classifiers by excluding them from the ensemble. We applied this method to an artificial dataset and sensor-based human activity datasets, with different sensor configurations and two types of noise (additive and rotational on inertial sensors). We compared our method with two other well-known approaches, generalized likelihood ratio (GLR) and One-Class Support Vector Machine (OCSVM), which detect anomalies at data/feature level. We found that our method is comparable with GLR and OCSVM. The advantages of our method compared to them is that it avoids monitoring raw data or features and only takes into account the decisions that are made by their classifiers, therefore it is independent of sensor modality and nature of anomaly. On the other hand, we found that OCSVM is very sensitive to the chosen parameters and furthermore in different types of anomalies it may react differently. In this paper we discuss the application domains which benefit from our method.