  Detecting outliers or anomalies is a common data analysis task. As a
sub-field of unsupervised machine learning, a large variety of approaches
exist, but the vast majority treats the input features as independent and often
fails to recognize even simple (linear) relationships in the input feature
space. Hence, we introduce RECol, a generic data pre-processing approach to
generate additional columns in a leave-one-out-fashion: For each column, we try
to predict its values based on the other columns, generating reconstruction
error columns. We run experiments across a large variety of common baseline
approaches and benchmark datasets with and without our RECol pre-processing
method and show that the generated reconstruction error feature space generally
seems to support common outlier detection methods and often considerably
improves their ROC-AUC and PR-AUC values.
