Outlier scores provided by different outlier models differ widely in their meaning, range, and contrast between different outlier models and, hence, are not easily comparable or interpretable. We propose a unification of outlier scores provided by various outlier models and a translation of the arbitrary "outlier factors" to values in the range [0, 1] interpretable as values describing the probability of a data object of being an outlier. As an application, we show that this unification facilitates enhanced ensembles for outlier detection.
In REF Author propose a unification of outlier scores provided by various outlier models and a translation of the arbitrary "outlier factors" to values in the range [0,1] interpretable as values describing the probability of a data object of being an outlier.
Interpreting and Unifying Outlier Scores
SDM
Computer Science