Data perturbation for outlier detection ensembles

Outlier detection and ensemble learning are well established research directions in data mining yet the application of ensemble techniques to outlier detection has been rarely studied. Building an ensemble requires learning of diverse models and combining these diverse models in an appropriate way. We propose data perturbation as a new technique to induce diversity in individual outlier detectors as well as a rank accumulation method for the combination of the individual outlier rankings in order to construct an outlier detection ensemble. In an extensive evaluation, we study the impact, potential, and shortcomings of this new approach for outlier detection ensembles. We show that this ensemble can significantly improve over weak performing base methods.