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

Version vom 26. November 2019, 15:55 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. Therefore, 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 a suitable model. Moreover, we are interested in applying PRIM with Active Learning to classification as well as regression problems, utilizing different query strategies. Acquiring labels for a given dataset can be quite costly, with active learning, only a small part of the dataset has to be labeled. A preliminary experiment indicated, that the combination of these methods, does indeed help reduce the necessary runs even further. Though we only tried one possible sampling method thus far. Finding the best-suited combinations of components for the above described non-trivial problems is the focus of this Thesis.

@@ 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-comprehensible. Yet PRIM alone requires relatively large datasets. It has been shown, that combining PRIM with  ML models (e.g. Random Forrest), which generalize faster, can reduce the number of simulation runs 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. Therefore, 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 are trying to reduce the number of simulation runs even further, using an active learning approach to train the model.
+We will try to reduce the number of simulation runs even further, using an active learning approach to train a suitable model.
-Moreover, we are interested in applying this approach to classification as well as regression problems, utilizing different query strategies.
+Moreover, we are interested in applying PRIM with Active Learning to classification as well as regression problems, utilizing different query strategies. Acquiring labels for a given dataset can be quite costly, with active learning, only a small part of the dataset has to be labeled. A preliminary experiment indicated, that the combination of these methods, does indeed help reduce the necessary runs even further. Though we only tried one possible sampling method thus far. Finding the best-suited combinations of components for the above described non-trivial problems is the focus of this Thesis.
-Acquiring labels for a given dataset can be quite costly, with active learning, only a small part of the dataset has to be labeled. A preliminary experiment indicated, that the combination of these methods, does indeed help reduce the necessary runs even further. Though we only tried one possible sampling method thus far. Optimizing this non-trivial task is the focus of this Thesis.
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