  We study iterative methods for (two-stage) robust combinatorial optimization
problems with discrete uncertainty. We propose a machine-learning-based
heuristic to determine starting scenarios that provide strong lower bounds. To
this end, we design dimension-independent features and train a Random Forest
Classifier on small-dimensional instances. Experiments show that our method
improves the solution process for larger instances than contained in the
training set and also provides a feature importance-score which gives insights
into the role of scenario properties.
