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Evaluation of Association Rules Extracted during Anomaly Explanation
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SYSNO ASEP 0447917 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Evaluation of Association Rules Extracted during Anomaly Explanation Tvůrce(i) Kopp, Martin (UIVT-O) RID
Holeňa, Martin (UIVT-O) SAI, RIDZdroj.dok. Proceedings ITAT 2015: Information Technologies - Applications and Theory. - Aachen & Charleston : Technical University & CreateSpace Independent Publishing Platform, 2015 / Yaghob J. - ISSN 1613-0073 - ISBN 978-1-5151-2065-0 Rozsah stran s. 143-149 Poč.str. 7 s. Forma vydání Online - E Akce ITAT 2015. Conference on Theory and Practice of Information Technologies /15./ Datum konání 17.09.2015-21.09.2015 Místo konání Slovenský Raj Země SK - Slovensko Typ akce EUR Jazyk dok. eng - angličtina Země vyd. DE - Německo Klíč. slova anomaly detection ; anomaly interpretation ; association rules ; confidence boost ; random forest Vědní obor RIV IN - Informatika CEP GA13-17187S GA ČR - Grantová agentura ČR Institucionální podpora UIVT-O - RVO:67985807 EID SCOPUS 84944348574 Anotace Discovering anomalies within data is nowadays very important, because it helps to uncover interesting events. Consequently, a considerable amount of anomaly detection algorithms was proposed in the last few years. Only a few papers about anomaly detection at least mentioned why some samples were labelled as anomalous. Therefore, we proposed a method allowing to extract rules explaining the anomaly from an ensemble of specifically trained decision trees, called sapling random forest. Our method is able to interpret the output of an arbitrary anomaly detector. The explanation is given as conjunctions of atomic conditions, which can be viewed as antecedents of association rules. In this work we focus on selection, post processing and evaluation of those rules. The main goal is to present a small number of the most important rules. To achieve this, we use quality measures such as lift and confidence boost. The resulting sets of rules are experimentally and empirically evaluated on two artificial datasets and one real-world dataset. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2016
Počet záznamů: 1