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Termin (Alle Termine)
Datum Fr 29. November 2019, 11:30 Uhr
Dauer 20 min
Raum Raum 010 (Gebäude 50.34)
Vorheriger Termin Fr 22. November 2019
Nächster Termin Fr 6. Dezember 2019

Vorträge

Vortragende(r) Emmanouil Emmanouilidis
Titel Patient Rule Induction Method with Active Learning
Vortragstyp Proposal
Betreuer(in) Vadim Arzamasov
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%.

We will try to reduce the number of simulation runs even further, using an active learning approach to train an intermediate ML model. Additionally, we extend the previously proposed methodology to not only cover classification but also regression problems. A preliminary experiment indicated, that the combination of these methods, does indeed help reduce the necessary runs even further. In this thesis, I will analyze different AL sampling strategies together with several intermediate ML models 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.

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