University of Twente Student Theses
Regularization Parameter Optimization in NIPA using Simulated Annealing
Hoven, T.D. (2024) Regularization Parameter Optimization in NIPA using Simulated Annealing.
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| Abstract: | Understanding and predicting outbreaks of epidemics has become a major focus since COVID-19. Researchers have explored various methods, from basic curve fitting to complex machine learning techniques, to predict how the virus spreads. One promising method is the Network Inference-based Prediction Algorithm (NIPA), which uses the SIR-model and the least absolute shrinkage and selection operator to estimate how the infections spread over different regions. However, fine-tuning the regularization parameter of NIPA can be complicated because of the time-consuming process and sub-optimal result of k-fold Cross-Validation (CV). To overcome this, we suggest using Simulated Annealing (SA) to optimize NIPA’s regularization parameter. Our study aims to combine SA with NIPA to make the process of choosing the optimal value for the parameter more effective. The results of the research show that the accuracy is improved and therefore indicate that SA is an acceptable alternative to CV, regardless of the computation time being higher. This research has found a method that can benefit epidemic modeling and prediction efforts. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Business & IT BSc (56066) |
| Link to this item: | https://purl.utwente.nl/essays/101044 |
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