%0 Generic
%T A flexible outlier detector based on a topology given by graph communities
%A Oriol Ramos Terrades
%A Albert Berenguel
%A Debora Gil
%D 2020
%F Oriol Ramos Terrades2020
%O IAM; DAG; 600.139; 600.145; 600.140; 600.121
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3475), last updated on Thu, 28 Jan 2021 10:24:19 +0100
%X Outlier, or anomaly, detection is essential for optimal performance of machine learning methods and statistical predictive models. It is not just a technical step in a data cleaning process but a key topic in many fields such as fraudulent document detection, in medical applications and assisted diagnosis systems or detecting security threats. In contrast to population-based methods, neighborhood based local approaches are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. However, a main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters. This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world data sets show that our approach overall outperforms, both, local and global strategies in multi and single view settings.
%9 miscellaneous
%U https://arxiv.org/abs/2002.07791
%U http://refbase.cvc.uab.es/files/RBG2020.pdf