Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
ACM Computing Surveys (CSUR)
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Adaptive mixtures of local experts
Neural Computation
A spatio-temporal Bayesian network classifier for understanding visual field deterioration
Artificial Intelligence in Medicine
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Data modeling using Bayesian Networks (BNs) has been investigated in depth for many years. More recently, Dynamic Bayesian Networks (DBNs) have been developed to deal with longitudinal datasets and exploit time dependent relationships in data. Our approach makes a further step in this context, by integrating into the BN framework a dynamic on-line data-selection process. The aims are to efficiently remove noisy data points in order to identify and model the key stages in a temporal process and to obtain better performance in classification. We tested our approach, called Dynamic Stage Bayesian Networks (DSBN), in the complex context of glaucoma functional tests, in which the available data is typically noisy and irregularly spaced. We compared the performances of DSBN with a static BN and a standard DBN. We also explored the potential of the technique by testing on another dataset from the Transport of London database. The results are promising and the potential of the technique is considerable.