2 research outputs found
LSCP: Locally Selective Combination in Parallel Outlier Ensembles
In unsupervised outlier ensembles, the absence of ground truth makes the
combination of base outlier detectors a challenging task. Specifically,
existing parallel outlier ensembles lack a reliable way of selecting competent
base detectors, affecting accuracy and stability, during model combination. In
this paper, we propose a framework---called Locally Selective Combination in
Parallel Outlier Ensembles (LSCP)---which addresses the issue by defining a
local region around a test instance using the consensus of its nearest
neighbors in randomly selected feature subspaces. The top-performing base
detectors in this local region are selected and combined as the model's final
output. Four variants of the LSCP framework are compared with seven widely used
parallel frameworks. Experimental results demonstrate that one of these
variants, LSCP_AOM, consistently outperforms baselines on the majority of
twenty real-world datasets.Comment: Proceedings of the 2019 SIAM International Conference on Data Mining
(SDM