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Termin (Alle Termine)
Datum Fr 16. August 2019, 11:30 Uhr
Dauer 30 min
Ort Raum 348 (Gebäude 50.34)
Vorheriger Termin Fr 9. August 2019
Nächster Termin Fr 23. August 2019


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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