  We hypothesize that similar objects should have similar outlier scores. To
our knowledge, all existing outlier detectors calculate the outlier score for
each object independently regardless of the outlier scores of the other
objects. Therefore, they do not guarantee that similar objects have similar
outlier scores. To verify our proposed hypothesis, we propose an outlier score
post-processing technique for outlier detectors, called neighborhood
averaging(NA), which pays attention to objects and their neighbors and
guarantees them to have more similar outlier scores than their original scores.
Given an object and its outlier score from any outlier detector, NA modifies
its outlier score by combining it with its k nearest neighbors' scores. We
demonstrate the effectivity of NA by using the well-known k-nearest neighbors
(k-NN). Experimental results show that NA improves all 10 tested baseline
detectors by 13% (from 0.70 to 0.79 AUC) on average evaluated on nine
real-world datasets. Moreover, even outlier detectors that are already based on
k-NN are also improved. The experiments also show that in some applications,
the choice of detector is no more significant when detectors are jointly used
with NA, which may pose a challenge to the generally considered idea that the
data model is the most important factor. We open our code on www.outlierNet.com
for reproducibility.
