Predictability of Classification Performance Measures with Meta-Learning
| Vortragende(r) | Huijie Wang | |
|---|---|---|
| Vortragstyp | Proposal | |
| Betreuer(in) | Jakob Bach | |
| Termin | Fr 12. April 2019 | |
| Vortragssprache | ||
| Vortragsmodus | ||
| Kurzfassung | In machine learning, classification is the problem of identifying to which of a set of categories a new instance belongs. Usually, we cannot tell how the model performs until it is trained. Meta-learning, which learns about the learning algorithms themselves, can predict the performance of a model without training it based on meta-features of datasets and performance measures of previous runs. Though there is a rich variety of meta-features and performance measures on meta-learning, existing works usually focus on which meta-features are likely to correlate with model performance using one particular measure. The effect of different types of performance measures remain unclear as it is hard to draw a comparison between results of existing works, which are based on different meta-data sets as well as meta-models. The goal of this thesis is to study if certain types of performance measures can be predicted better than other ones and how much does the choice of the meta-model matter, by constructing different meta-regression models on same meta-features and different performance measures. We will use an experimental approach to evaluate our study. | |