R3-4: Model Selection and Evaluation in Supevised Machine Learning

PhD student
Max Westphal
Primary supervisor
Werner Brannath

Project description

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.