Institutsseminar/2019-08-16
Version vom 7. Mai 2019, 13:50 Uhr von Erik Burger (Diskussion | Beiträge) (Die Seite wurde neu angelegt: „{{Termin |datum=2019/08/16 11:30:00 |raum=Raum 348 (Gebäude 50.34) }}“)
| Datum | Fr 16. August 2019, 11:30 Uhr | |
|---|---|---|
| Dauer | 30 min | |
| Raum | Raum 348 (Gebäude 50.34) | |
| Vorheriger Termin | Fr 9. August 2019 | |
| Nächster Termin | Fr 23. August 2019 |
Vorträge
| Vortragende(r) | Huijie Wang | |
|---|---|---|
| Titel | Predictability of Classfication Performance Measures with Meta-Learning | |
| Vortragstyp | Bachelorarbeit | |
| Betreuer(in) | Jakob Bach | |
| Kurzfassung | Choosing a suitable classifier for a given dataset is an important part in the process of solving a classification problem. Meta-learning, which learns about the learning algorithms themselves, can predict the performance of a classifier without training it. The effect of different types of performance measures remains unclear, as it is hard to draw a comparison between results of existing works, which are based on different meta-datasets as well as meta-models. In this thesis, we study the predictability of different classification performance measures with meta-learning, also we compare the performances of meta-learning using different meta-regression models. We conduct experiments with meta-datasets from previous studies considering 11 meta-targets and 6 meta-models. Additionally, we study the relation between different groups of meta-features and the performance of meta-learning. Results of our experiments show that meta-targets have similar predictability and the choice of meta-model has a big impact on the performance of meta-learning. | |
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