One-class classification in the presence of point, collective, and contextual anomalies

Anomaly detection has a prominent position in the processing pipeline of any real-world data-driven application. Its central goal is to detect and separate valid data points from malicious-anomalous-ones such that the cleaned data set can be processed further. In many applications, anomalies are even the prime objects of interest and need to be exposed early in order to avoid loss, e.g. in credit card fraud detection. One-class classification is a machine learning concept that is especially suited for the anomaly detection problem. Intrinsically unsupervised, it aims at providing a concise description of a given data set such that data points generated by a different process can be detected accurately. Prominent machine learning models for one-class classification are one-class support vector machines and the closely related support vector data descriptions. The contribution of this thesis is the extension of those methods to cope with different scenarios of anomalies: - Point Anomalies: Assuming that anomalies are scarce and occur independently of each other, methods for controlling the sparsity of the found solutions in terms of single independent features and groups of features are derived. - Collective Anomalies: In this scenario anomalies are assumed to appear as groups of measurements instead of single entries. Techniques from structured output learning are (i) extended to cope with large-scale problems, (ii) employed to derive an unsupervised anomaly detector for groups of measurements that exhibit a latent dependency structure. - Contextual Anomalies: Anomalies appear only in specific contexts and data is supposed to carry two signals that contain behavioral and contextual information. Contributions in this scenario consider latent class dependencies and are threefold: (i) the derivation of a method capable of detecting latent class contextual anomalies, (ii) theoretical insight reveal \kmeans as a special case, and (iii) a method for learning with latent class dependencies when an additional structure is imposed on the latent variables. The proposed methods are empirically analyzed on a variety of different applications ranging from gene finding to porosity estimation to brain computer interfaces showing promising performance when compared to baseline methods.

Author dc:creator

• Görnitz, Nico

Advisor dc:contributor.advisor

• Müller, Klaus-Robert

Licence dc:rights.uri

• https://creativecommons.org/licenses/by/4.0/

Language dc:language.iso

en

Identifier URI

http://dx.doi.org/10.14279/depositonce-7897

OAI identifier oai:identifier

oai:depositonce.tu-berlin.de:11303/8768