Explaining Anomalies with Sapling Random Forests

0432430 - ÚI 2015 RIV CZ eng C - Conference Paper (international conference)
Pevný, T. - Kopp, Martin
Explaining Anomalies with Sapling Random Forests.
ITAT 2014. Information Technologies - Applications and Theory. Part II. Prague: Institute of Computer Science AS CR, 2014 - (Kůrková, V.; Bajer, L.; Peška, L.; Vojtáš, R.; Holeňa, M.; Nehéz, M.), s. 71-78. ISBN 978-80-87136-19-5.
[ITAT 2014. European Conference on Information Technologies - Applications and Theory /14./. Demänovská dolina (SK), 25.09.2014-29.09.2014]
R&D Projects: GA ČR GA13-17187S
Grant - others:GA ČR(CZ) GPP103/12/P514
Institutional support: RVO:67985807
Keywords : anomaly explanation * decision trees * feature selection * random forest
Subject RIV: BB - Applied Statistics, Operational Research

The main objective of anomaly detection algorithms is finding samples deviating from the majority. Although a vast number of algorithms designed for this already exist, almost none of them explain, why a particular sample was labelled as an anomaly. To address this issue, we propose an algorithm called Explainer, which returns the explanation of sample’s differentness in disjunctive normal form (DNF), which is easy to understand by humans. Since Explainer treats anomaly detection algorithms as black-boxes, it can be applied in many domains to simplify investigation of anomalies. The core of Explainer is a set of specifically trained trees, which we call sapling random forests. Since their training is fast and memory efficient, the whole algorithm is lightweight and applicable to large databases, datastreams, and real-time problems. The correctness of Explainer is demonstrated on a wide range of synthetic and real world datasets.
Permanent Link: http://hdl.handle.net/11104/0236783