  Exploratory landscape analysis (ELA) is a well-established tool to
characterize optimization problems via numerical features. ELA is used for
problem comprehension, algorithm design, and applications such as automated
algorithm selection and configuration. Until recently, however, ELA was limited
to search spaces with either continuous or discrete variables, neglecting
problems with mixed variable types. This gap was addressed in a recent study
that uses an approach based on target-encoding to compute exploratory landscape
features for mixedvariable problems. In this work, we investigate an
alternative encoding scheme based on SHAP values. While these features do not
lead to better results in the algorithm selection setting considered in
previous work, the two different encoding mechanisms exhibit complementary
performance. Combining both feature sets into a hybrid approach outperforms
each encoding mechanism individually. Finally, we experiment with two different
ways of meta-selecting between the two feature sets. Both approaches are
capable of taking advantage of the performance complementarity of the models
trained on target-encoded and SHAP-encoded feature sets, respectively.
