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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F14%3A00219644%21RIV15-MSM-21230___
rdf:type
skos:Concept n20:Vysledek
rdfs:seeAlso
http://www.library.sk/i2/content.csg.cls?ictx=cav&repo=crepo1&key=70202320092
dcterms:description
The main objective of anomaly detection algo- rithms is finding samples deviating from the majority. Al- though a vast number of algorithms designed for this al- ready exist, almost none of them explain, why a particular sample was labelled as an anomaly. To address this is- sue, we propose an algorithm called Explainer, which re- turns the explanation of sample’s differentness in disjunc- tive normal form (DNF), which is easy to understand by humans. Since Explainer treats anomaly detection algo- rithms 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, data- streams, and real-time problems. The correctness of Ex- plainer is demonstrated on a wide range of synthetic and real world datasets. The main objective of anomaly detection algo- rithms is finding samples deviating from the majority. Al- though a vast number of algorithms designed for this al- ready exist, almost none of them explain, why a particular sample was labelled as an anomaly. To address this is- sue, we propose an algorithm called Explainer, which re- turns the explanation of sample’s differentness in disjunc- tive normal form (DNF), which is easy to understand by humans. Since Explainer treats anomaly detection algo- rithms 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, data- streams, and real-time problems. The correctness of Ex- plainer is demonstrated on a wide range of synthetic and real world datasets.
dcterms:title
Explaining Anomalies with Sapling Random Forests Explaining Anomalies with Sapling Random Forests
skos:prefLabel
Explaining Anomalies with Sapling Random Forests Explaining Anomalies with Sapling Random Forests
skos:notation
RIV/68407700:21230/14:00219644!RIV15-MSM-21230___
n3:aktivita
n13:I n13:S n13:P
n3:aktivity
I, P(GA13-17187S), P(GPP103/12/P514), S
n3:dodaniDat
n8:2015
n3:domaciTvurceVysledku
n12:3818489 n12:1260650
n3:druhVysledku
n9:D
n3:duvernostUdaju
n22:S
n3:entitaPredkladatele
n18:predkladatel
n3:idSjednocenehoVysledku
16067
n3:idVysledku
RIV/68407700:21230/14:00219644
n3:jazykVysledku
n7:eng
n3:klicovaSlova
Anomaly explanation; decision trees; feature selection; random forest
n3:klicoveSlovo
n14:random%20forest n14:Anomaly%20explanation n14:decision%20trees n14:feature%20selection
n3:kontrolniKodProRIV
[D6E3C2D9B1C5]
n3:mistoKonaniAkce
Demänovská Dolina
n3:mistoVydani
Praha
n3:nazevZdroje
Proceedings of the 14th conference ITAT 2014 – Workshops and Posters
n3:obor
n15:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n4:GA13-17187S n4:GPP103%2F12%2FP514
n3:rokUplatneniVysledku
n8:2014
n3:tvurceVysledku
Pevný, Tomáš Kopp, Martin
n3:typAkce
n21:WRD
n3:zahajeniAkce
2014-09-25+02:00
s:numberOfPages
8
n17:hasPublisher
Ústav informatiky AV ČR
n19:isbn
978-80-87136-19-5
n11:organizacniJednotka
21230