Number of the records: 1
Anomaly explanation with random forests
- 1.
SYSNO ASEP 0522404 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Anomaly explanation with random forests Author(s) Kopp, M. (CZ)
Pevný, T. (CZ)
Holeňa, Martin (UIVT-O) SAI, RIDArticle number 113187 Source Title Expert Systems With Applications. - : Elsevier - ISSN 0957-4174
Roč. 149, 1 July (2020)Number of pages 16 s. Publication form Print - P Language eng - English Country GB - United Kingdom Keywords Anomaly detection ; Anomaly explanation ; Classification rules ; Feature selection ; Random forests Subject RIV IN - Informatics, Computer Science OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects GA17-01251S GA ČR - Czech Science Foundation (CSF) Method of publishing Limited access Institutional support UIVT-O - RVO:67985807 UT WOS 000525819400001 EID SCOPUS 85078848410 DOI 10.1016/j.eswa.2020.113187 Annotation 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2021 Electronic address http://dx.doi.org/10.1016/j.eswa.2020.113187
Number of the records: 1