Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Fractals and disordered systems
Fractals and disordered systems
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Wearable and automotive systems for affect recognition from physiology
Wearable and automotive systems for affect recognition from physiology
Structure Search and Stability Enhancement of Bayesian Networks
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mathematical Techniques in Multisensor Data Fusion (Artech House Information Warfare Library)
Mathematical Techniques in Multisensor Data Fusion (Artech House Information Warfare Library)
Advanced Methods And Tools for ECG Data Analysis
Advanced Methods And Tools for ECG Data Analysis
A genetic algorithm-based method for feature subset selection
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Multisensor Data Fusion
Multi-Sensor Data Fusion: An Introduction
Multi-Sensor Data Fusion: An Introduction
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Remote physiological monitoring of first responders with intermittent network connectivity
WH '10 Wireless Health 2010
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Multiple sensor fusion is very important for wireless health monitoring since a single type of sensor usually can only provide limited aspects of the health condition while multiple sensors of different types hopefully can complement each other and yield more comprehensive aspects of the health condition. Many existing sensor fusion approaches are based on a flat structure, where multiple sensor features are treated as in the same layer and are fused by the feature-level fusion. In this paper we present a systematic approach using a structurally learned Bayesian Network (BN) for sensor fusion. The BN serves as a powerful framework that can integrate multiple sensor features in a hierarchy that is automatically learned via supervised learning. We present a hybrid structure learning approach that includes four steps and consists of both systematic global and local structure learning, as well as random perturbation for structure learning. Subsequent to the feature selection, we first learn an Augmented Bayesian Classifier (ABC) and it is followed by an extended K2 structure learning to search for a better structure in another structure subspace. Random structure learning is then performed to perturb the structure learning so as to avoid getting stuck in a local optimum. Finally, we perform local structure learning with hill-climbing by reversing or removing each link between features. The proposed hierarchical sensor fusion solution outperformed some conventional approaches such as Naïve Bayesian Classifier and Support Vector Machine classifier that integrate multiple sensor features by a flat feature-level fusion.