The Dynamic Stage Bayesian Network: Identifying and Modelling Key Stages in a Temporal Process

Abstract

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.