  We consider the problem of classifying business process instances based on
structural features derived from event logs. The main motivation is to provide
machine learning based techniques with quick response times for interactive
computer assisted root cause analysis. In particular, we create structural
features from process mining such as activity and transition occurrence counts,
and ordering of activities to be evaluated as potential features for
classification. We show that adding such structural features increases the
amount of information thus potentially increasing classification accuracy.
However, there is an inherent trade-off as using too many features leads to too
long run-times for machine learning classification models. One way to improve
the machine learning algorithms' run-time is to only select a small number of
features by a feature selection algorithm. However, the run-time required by
the feature selection algorithm must also be taken into account. Also, the
classification accuracy should not suffer too much from the feature selection.
The main contributions of this paper are as follows: First, we propose and
compare six different feature selection algorithms by means of an experimental
setup comparing their classification accuracy and achievable response times.
Second, we discuss the potential use of feature selection results for computer
assisted root cause analysis as well as the properties of different types of
structural features in the context of feature selection.
