  In order to allow machine learning algorithms to extract knowledge from raw
data, these data must first be cleaned, transformed, and put into
machine-appropriate form. These often very time-consuming phase is referred to
as preprocessing. An important step in the preprocessing phase is feature
selection, which aims at better performance of prediction models by reducing
the amount of features of a data set. Within these datasets, instances of
different events are often imbalanced, which means that certain normal events
are over-represented while other rare events are very limited. Typically, these
rare events are of special interest since they have more discriminative power
than normal events. The aim of this work was to filter instances provided to
feature selection methods for these rare instances, and thus positively
influence the feature selection process. In the course of this work, we were
able to show that this filtering has a positive effect on the performance of
classification models and that outlier detection methods are suitable for this
filtering. For some data sets, the resulting increase in performance was only a
few percent, but for other datasets, we were able to achieve increases in
performance of up to 16 percent. This work should lead to the improvement of
the predictive models and the better interpretability of feature selection in
the course of the preprocessing phase. In the spirit of open science and to
increase transparency within our research field, we have made all our source
code and the results of our experiments available in a publicly available
repository.
