  For data sets with similar features, for example highly correlated features,
most existing stability measures behave in an undesired way: They consider
features that are almost identical but have different identifiers as different
features. Existing adjusted stability measures, that is, stability measures
that take into account the similarities between features, have major
theoretical drawbacks. We introduce new adjusted stability measures that
overcome these drawbacks. We compare them to each other and to existing
stability measures based on both artificial and real sets of selected features.
Based on the results, we suggest using one new stability measure that considers
highly similar features as exchangeable.
