Anomaly explanation with random forests

0522404 - ÚI 2021 RIV GB eng J - Journal Article
Kopp, M. - Pevný, T. - Holeňa, Martin
Anomaly explanation with random forests.
Expert Systems With Applications. Roč. 149, 1 July (2020), č. článku 113187. ISSN 0957-4174. E-ISSN 1873-6793
R&D Projects: GA ČR GA17-01251S
Grant - others:GA ČR(CZ) GA18-21409S
Program: GA
Institutional support: RVO:67985807
Keywords : Anomaly detection * Anomaly explanation * Classification rules * Feature selection * Random forests
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 6.954, year: 2020
Method of publishing: Limited access
http://dx.doi.org/10.1016/j.eswa.2020.113187

Anomaly detection has become an important topic in many domains with many different solutions proposed until now. Despite that, there are only a few anomaly detection methods trying to explain how the sample differs from the rest. This work contributes to filling this gap because knowing why a sample is considered anomalous is critical in many application domains. The proposed solution uses a specific type of random forests to extract rules explaining the difference, which are then filtered and presented to the user as a set of classification rules sharing the same consequent, or as the equivalent rule with an antecedent in a disjunctive normal form. The quality of that solution is documented by comparison with the state of the art algorithms on 34 real-world datasets.
Permanent Link: http://hdl.handle.net/11104/0306903