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Statements

Subject Item
n2:RIV%2F68407700%3A21240%2F14%3A00219641%21RIV15-MSM-21240___
rdf:type
skos:Concept n20:Vysledek
rdfs:seeAlso
http://www.library.sk/i2/content.csg.cls?ictx=cav&repo=crepo1&key=88442135003
dcterms:description
The main objective of outlier detection is find- ing samples considerably deviating from the majority. Such outliers, often referred to as anomalies, are nowadays more and more important, because they help to uncover in- teresting events within data. Consequently, a considerable amount of statistical and data mining techniques to iden- tify anomalies was proposed in the last few years, but only a few works at least mentioned why some sample was la- belled as an anomaly. Therefore, we propose a method based on specifically trained decision trees, called sapling random forest. Our method is able to interpret the output of arbitrary anomaly detector. The explanation is given as a subset of features, in which the sample is most deviating, or as con- junctions of atomic conditions, which can be viewed as antecedents of logical rules easily understandable by hu- mans. To simplify the investigation of suspicious samples even more, we propose two methods of clustering anoma- lies into groups. Such clusters can be investigated at once saving time and human efforts. The feasibility of our ap- proach is demonstrated on several synthetic and one real world datasets. The main objective of outlier detection is find- ing samples considerably deviating from the majority. Such outliers, often referred to as anomalies, are nowadays more and more important, because they help to uncover in- teresting events within data. Consequently, a considerable amount of statistical and data mining techniques to iden- tify anomalies was proposed in the last few years, but only a few works at least mentioned why some sample was la- belled as an anomaly. Therefore, we propose a method based on specifically trained decision trees, called sapling random forest. Our method is able to interpret the output of arbitrary anomaly detector. The explanation is given as a subset of features, in which the sample is most deviating, or as con- junctions of atomic conditions, which can be viewed as antecedents of logical rules easily understandable by hu- mans. To simplify the investigation of suspicious samples even more, we propose two methods of clustering anoma- lies into groups. Such clusters can be investigated at once saving time and human efforts. The feasibility of our ap- proach is demonstrated on several synthetic and one real world datasets.
dcterms:title
Interpreting and clustering outliers with sapling random forests Interpreting and clustering outliers with sapling random forests
skos:prefLabel
Interpreting and clustering outliers with sapling random forests Interpreting and clustering outliers with sapling random forests
skos:notation
RIV/68407700:21240/14:00219641!RIV15-MSM-21240___
n3:aktivita
n10:S n10:P n10:I
n3:aktivity
I, P(GA13-17187S), P(GPP103/12/P514), S
n3:dodaniDat
n12:2015
n3:domaciTvurceVysledku
n13:6036627
n3:druhVysledku
n9:D
n3:duvernostUdaju
n15:S
n3:entitaPredkladatele
n16:predkladatel
n3:idSjednocenehoVysledku
22532
n3:idVysledku
RIV/68407700:21240/14:00219641
n3:jazykVysledku
n4:eng
n3:klicovaSlova
Anomaly detection; anomaly interpretation; clustering; decision trees; feature selection; random forest
n3:klicoveSlovo
n5:random%20forest n5:Anomaly%20detection n5:anomaly%20interpretation n5:feature%20selection n5:decision%20trees n5:clustering
n3:kontrolniKodProRIV
[E3E607563AE6]
n3:mistoKonaniAkce
Demänovská Dolina
n3:mistoVydani
Praha
n3:nazevZdroje
Proceedings of the 14th conference ITAT 2014 – Workshops and Posters
n3:obor
n21:IN
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
3
n3:projekt
n19:GPP103%2F12%2FP514 n19:GA13-17187S
n3:rokUplatneniVysledku
n12:2014
n3:tvurceVysledku
Kopp, Martin Pevný, Tomáš Holeňa, Martin
n3:typAkce
n6:WRD
n3:zahajeniAkce
2014-09-25+02:00
s:numberOfPages
7
n17:hasPublisher
Ústav informatiky AV ČR
n18:isbn
978-80-87136-19-5
n11:organizacniJednotka
21240