TY  - STD
AU  - Oriol Ramos Terrades
AU  - Albert Berenguel
AU  - Debora Gil
PY  - 2020//
TI  - A flexible outlier detector based on a topology given by graph communities
N2  - 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.
UR  - https://arxiv.org/abs/2002.07791
L1  - http://refbase.cvc.uab.es/files/RBG2020.pdf
N1  - IAM; DAG; 600.139; 600.145; 600.140; 600.121
ID  - Oriol Ramos Terrades2020
ER  -