  An ensemble technique is characterized by the mechanism that generates the
components and by the mechanism that combines them. A common way to achieve the
consensus is to enable each component to equally participate in the aggregation
process. A problem with this approach is that poor components are likely to
negatively affect the quality of the consensus result. To address this issue,
alternatives have been explored in the literature to build selective classifier
and cluster ensembles, where only a subset of the components contributes to the
computation of the consensus. Of the family of ensemble methods, outlier
ensembles are the least studied. Only recently, the selection problem for
outlier ensembles has been discussed. In this work we define a new graph-based
class of ranking selection methods. A method in this class is characterized by
two main steps: (1) Mapping the rankings onto a graph structure; and (2) Mining
the resulting graph to identify a subset of rankings. We define a specific
instance of the graph-based ranking selection class. Specifically, we map the
problem of selecting ensemble components onto a mining problem in a graph. An
extensive evaluation was conducted on a variety of heterogeneous data and
methods. Our empirical results show that our approach outperforms
state-of-the-art selective outlier ensemble techniques.
