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Anomaly explanation with random forests
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SYSNO ASEP 0522404 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Anomaly explanation with random forests Tvůrce(i) Kopp, M. (CZ)
Pevný, T. (CZ)
Holeňa, Martin (UIVT-O) SAI, RIDČíslo článku 113187 Zdroj.dok. Expert Systems With Applications. - : Elsevier - ISSN 0957-4174
Roč. 149, 1 July (2020)Poč.str. 16 s. Forma vydání Tištěná - P Jazyk dok. eng - angličtina Země vyd. GB - Velká Británie Klíč. slova Anomaly detection ; Anomaly explanation ; Classification rules ; Feature selection ; Random forests Vědní obor RIV IN - Informatika Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) CEP GA17-01251S GA ČR - Grantová agentura ČR Způsob publikování Omezený přístup Institucionální podpora UIVT-O - RVO:67985807 UT WOS 000525819400001 EID SCOPUS 85078848410 DOI 10.1016/j.eswa.2020.113187 Anotace 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. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2021 Elektronická adresa http://dx.doi.org/10.1016/j.eswa.2020.113187
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