| Kurzfassung
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A ranking is the result of running an experiment, a set of encoders is applied to an
experimental condition (dataset, model, tuning, scoring) and are then ranked according to
their performance.
To draw conclusions about the performance of the encoders for a set of experimental
conditions, one can aggregate the rankings into a consensus ranking. (i.e. taking the median
rank)
The goal of the thesis is to explore the space of consensus rankings and find all possible
consensus rankings.
However, running an experiment is a very time-consuming task. Therefore we utilize Active
Learning, to avoid running unnecessary experiments. In Active Learning, the learner can
choose the data it is trained on and achieves greater accuracy with fewer labeled data.
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