Scenario Discovery with Active Learning
| Vortragende(r) | Emmanouil Emmanouilidis | |
|---|---|---|
| Vortragstyp | Bachelorarbeit | |
| Betreuer(in) | Vadim Arzamasov | |
| Termin | Fr 8. Mai 2020 | |
| Vortragssprache | ||
| Vortragsmodus | ||
| Kurzfassung | PRIM (Patient Rule Induction Method) is an algorithm for discovering scenarios from simulations, by creating hyperboxes, that are human-comprehensible. Yet PRIM alone requires relatively large datasets and computational simulations are usually quite expensive. Consequently, one wants to obtain a plausible scenario, with a minimal number of simulations. It has been shown, that combining PRIM with ML models, which generalize faster, can reduce the number of necessary simulation runs by around 75%.
In this thesis, I analyze different Active Learning (AL) sampling strategies together with several intermediate ML models, in order to find out if AL can systematically improve existing scenario discovery methods and if a most beneficial combination of sampling method and intermediate ML model exists for this purpose. | |