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

[[Institutsseminar/2020-05-08|

	Veranstaltungsdatum	Veranstaltungsraum
Institutsseminar/2020-05-08	Fr 8. Mai 2020, 11:05	Raum 348 (Gebäude 50.34)

]]

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.

@@ Zeile 5: / Zeile 5: @@
 |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.
 }}