R3-4: Model Selection and Evaluation in Supevised Machine Learning
- PhD student
- Max Westphal
- Primary supervisor
- Werner Brannath
The major target of research area R3 "Mathematical data analysis" is the development
of numerically efficient algorithms for low dimensional feature building and corresponding classification
methods that utilise high dimensional, sparse input data from applications like MALDI imaging.
During a typical (supervised machine-learning) project, several classification algorithms will be developed
that finally need to be fine-tuned and evaluated by real application studies. Furthermore, the
different methods need to be compared with regard to their operating characteristics. This leads to
the question of how to best design experimental studies for an adjustment, evaluation and comparison
of classification algorithms such that costs like study time and number of subjects are minimised.
This objective should be pursued under the requirement that bias, optimism and type I error inflations
are largely avoided.
This project is part of the KKSB.