Patient Rule Induction Method with Active Learning: Unterschied zwischen den Versionen

Aktuelle Version vom 26. November 2019, 18:44 Uhr

Vortragende(r)	Emmanouil Emmanouilidis
Vortragstyp	Proposal
Betreuer(in)	Vadim Arzamasov
Termin	Fr 29. November 2019
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%. 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.

@@ Zeile 5: / Zeile 5: @@
 |betreuer=Vadim Arzamasov
 |termin=Institutsseminar/2019-11-29 Zusatztermin
-|kurzfassung=PRIM (Patient Rule Induction Method) is an algorithm to create hyperboxes that are human comprehansable. But PRIM requires relatively large datasets. It has been shown, that using ML models (e.g. Random Forrest) that generalize faster can increase performance by around 75%.
+|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 we are trying to increase the overall performance even further, using an active learning approach in order to train the models. Acquiring labels for a given dataset can be quite costly, with active learning only a small part of the dataset has to ben labeled (if at all). Furthermore, a  preliminary experiment indicated, that combining these methods does indeed increase performance even further.
+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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