Patient Rule Induction Method with Active Learning: Unterschied zwischen den Versionen
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|vortragstyp=Proposal | |vortragstyp=Proposal | ||
|betreuer=Vadim Arzamasov | |betreuer=Vadim Arzamasov | ||
|termin=Institutsseminar/2019-11-29 | |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 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%. | ||
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. | 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. | ||
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Version vom 25. November 2019, 16:43 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 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%.
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. | |