A Flexible Outlier Detector Based on a Topology Given by Graph Communities
Ramos Terrades, Oriol (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació)
Berenguel Centeno, Albert (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació)
Gil, Debora (Centre de Visió per Computador (Bellaterra, Catalunya))

Data:	2022
Resum:	Outlier detection is essential for optimal performance of machine learning methods and statistical predictive models. Their detection is especially determinant in small sample size unbalanced problems, since in such settings outliers become highly influential and significantly bias models. This particular experimental settings are usual in medical applications, like diagnosis of rare pathologies, outcome of experimental personalized treatments or pandemic emergencies. In contrast to population-based methods, neighborhood based local approaches compute an outlier score from the neighbors of each sample, are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. 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, like the number of neighbors. 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 and synthetic data sets show that our approach outperforms, both, local and global strategies in multi and single view settings.
Ajuts:	Agencia Estatal de Investigación RTI2018-095645-B-C21 Agencia Estatal de Investigación RTI2018-095209-B-C21 Agencia Estatal de Investigación PID2021-126776OB-C21 Agencia Estatal de Investigación PID2021-12688OB-I00 Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-1624 Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-1783
Nota:	Altres ajuts: acords transformatius de la UAB
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Classification algorithms ; Detection algorithms ; Description of feature space local structure ; Graph communities ; Machine learning algorithms ; Outlier detectors
Publicat a:	Big Data Research, Vol. 29 (August 2022) , art. 100332, ISSN 2214-5796

DOI: 10.1016/j.bdr.2022.100332

10 p, 659.4 KB

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