Evaluation of Association Rules Extracted during Anomaly Explanation

0447917 - ÚI 2016 RIV DE eng C - Konferenční příspěvek (zahraniční konf.)
Kopp, Martin - Holeňa, Martin
Evaluation of Association Rules Extracted during Anomaly Explanation.
Proceedings ITAT 2015: Information Technologies - Applications and Theory. Aachen & Charleston: Technical University & CreateSpace Independent Publishing Platform, 2015 - (Yaghob, J.), s. 143-149. CEUR Workshop Proceedings, V-1422. ISBN 978-1-5151-2065-0. ISSN 1613-0073.
[ITAT 2015. Conference on Theory and Practice of Information Technologies /15./. Slovenský Raj (SK), 17.09.2015-21.09.2015]
Grant CEP: GA ČR GA13-17187S
Institucionální podpora: RVO:67985807
Klíčová slova: anomaly detection * anomaly interpretation * association rules * confidence boost * random forest
Kód oboru RIV: IN - Informatika

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.
Trvalý link: http://hdl.handle.net/11104/0249671