https://sdq.kastel.kit.edu/index.php?title=Predictability_of_Classfication_Performance_Measures_with_Meta-Learning&feed=atom&action=historyPredictability of Classfication Performance Measures with Meta-Learning - Versionsgeschichte2024-03-29T05:11:43ZVersionsgeschichte dieser Seite in SDQ-InstitutsseminarMediaWiki 1.39.6https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Predictability_of_Classfication_Performance_Measures_with_Meta-Learning&diff=1059&oldid=prevJakob.bach@kit.edu: Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Huijie Wang |email=upssh@student.kit.edu |vortragstyp=Bachelorarbeit |betreuer=Jakob Bach |termin=Institutsseminar/2019-08-16 |kurzfass…“2019-08-07T06:28:44Z<p>Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Huijie Wang |email=upssh@student.kit.edu |vortragstyp=Bachelorarbeit |betreuer=Jakob Bach |termin=Institutsseminar/2019-08-16 |kurzfass…“</p>
<p><b>Neue Seite</b></p><div>{{Vortrag<br />
|vortragender=Huijie Wang<br />
|email=upssh@student.kit.edu<br />
|vortragstyp=Bachelorarbeit<br />
|betreuer=Jakob Bach<br />
|termin=Institutsseminar/2019-08-16<br />
|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.<br />
}}</div>Jakob.bach@kit.edu