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Spatial Operators for Evolving Dynamic Bayesian Networks from Spatio-Temporal Data

Moorfields Eye Hospital NHS Trust. Spatial Operators for Evolving Dynamic Bayesian Networks from Spatio-Temporal Data. Allan Tucker Xiaohui Liu David Garway-Heath. Contents of Talk. Introduction to BNs, DBNs, and SDBNs Visual Field Data Representation and Spatial Operators

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Spatial Operators for Evolving Dynamic Bayesian Networks from Spatio-Temporal Data

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  1. Moorfields Eye Hospital NHS Trust Spatial Operators for Evolving Dynamic Bayesian Networks from Spatio-Temporal Data Allan Tucker Xiaohui Liu David Garway-Heath

  2. Contents of Talk • Introduction to BNs, DBNs, and SDBNs • Visual Field Data • Representation and Spatial Operators • The Experiments • Results (Inc. Demo of the Operators) • Conclusions

  3. BNs, DBNs and SDBNs

  4. Visual Field Data • Collected From an Extensive Study • Investigating OHT • VF Tests carried out approximately every month • 54 Points on the VF including two on the Blind Spot • 95 Patients (1809 measurements in all)

  5. Visual Field Data

  6. The Datasets • Visual Field Data • 54 Variables, 95 Patients, 1809 Time Points • Synthetic Data • 64 DBN Variables Representing 8x8 Grid • Parents: 1st Order Cartesian Neighbours with Time Lag of 1 • Each Node has Gaussian CPT

  7. Representation and Operators • Population Represents the Solution • Individual Represents Point in Space and its Dependencies • Efficient Use of Calls to Fitness • Spatial, Non-Spatial and Temporal Operators Applied to Individuals

  8. Representation {{ax,ay,l}, {ax,ay,l}, {ax,ay,l}} {{ax,ay,l}, {ax,ay,l}} {{ax,ay,l}, {ax,ay,l}, {ax,ay,l}}

  9. Spatial Operators

  10. The Experiments • Spatial Operators Only • Non-Spatial Operators Only • Both Sets of Operators • Investigate Learning Curves (Log-Lik) and Operator Success Rate • Compare to Strawman Greedy Search • Investigate SD, and Expert Knowledge

  11. Results – Synthetic Data • Spatial Operators Only Perform the Best • Non-Spatial and K2 are the Worst • Non-Spatial Appears to Eventually Discover a ‘Good’ Structure

  12. Results – Synthetic Data • Most Successful Operator by far is SpatAdd • Take, and SpatMut are also Good • SpatCross Looks Bad (Few Successes’) • But Accounts for Biggest Fitness Improvements

  13. Results – Visual Field Data • This Time All-Operators Performs Best • Closely Followed by Spatial Only • But Given Time Non Spatial Catch Up • K2 Performs Very Poorly

  14. Results – Visual Field Data • Again SpatAdd, Take, and SpatMut are Best • SpatCross Looks Better But Still Least Successes • Again Accounts for Biggest Fitness Improvements

  15. Results

  16. Results

  17. Spatial Operator Demo 1

  18. Spatial Operator Demo 2

  19. Spatial Operator Demo 3

  20. Spatial Operator Demo 4

  21. Spatial Operator Demo 5

  22. Conclusions • Developed Evolutionary Operators Specifically Designed for Spatial Data • Efficient Representation • Perform Competitively Compared to Standard Operators on Synthetic and Real World Data • Generates VF SDBNs Consistent with Experts

  23. Future Work • Explore Other Spatial Datasets e.g. Rainfall • Investigate Other Methods Developed for Spatial NN Function – EDAs • Extend the VF Model to Include Both Eyes and Clinical Information

  24. Any Questions?

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