Scenario Discovery with Active Learning: Unterschied zwischen den Versionen

Version vom 5. Mai 2020, 17:19 Uhr

Vortragende(r)	Emmanouil Emmanouilidis
Vortragstyp	Bachelorarbeit
Betreuer(in)	Vadim Arzamasov
Termin	Fr 8. Mai 2020
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.

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 |betreuer=Vadim Arzamasov
 |termin=Institutsseminar/2020-05-08
-|kurzfassung=TBA
+|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.
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