About: Interpreting and clustering outliers with sapling random forests

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Interpreting and clustering outliers with sapling random forests Goto Sponge NotDistinct Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://www.library.sk/i2/content.csg.cls?ictx=cav&repo=crepo1&key=88442135003
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. (en)
Title	Interpreting and clustering outliers with sapling random forests Interpreting and clustering outliers with sapling random forests (en)
skos:prefLabel	Interpreting and clustering outliers with sapling random forests Interpreting and clustering outliers with sapling random forests (en)
skos:notation	RIV/68407700:21240/14:00219641!RIV15-MSM-21240___
http://linked.open...avai/riv/aktivita	I P S
http://linked.open...avai/riv/aktivity	I, P(GA13-17187S), P(GPP103/12/P514), S
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Holeňa, Martin
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta informačních technologií
http://linked.open...dnocenehoVysledku	22532
http://linked.open...ai/riv/idVysledku	RIV/68407700:21240/14:00219641
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Anomaly detection; anomaly interpretation; clustering; decision trees; feature selection; random forest (en)
http://linked.open.../riv/klicoveSlovo	Anomaly detection clustering decision trees anomaly interpretation feature selection random forest
http://linked.open...ontrolniKodProRIV	[E3E607563AE6]
http://linked.open...v/mistoKonaniAkce	Demänovská Dolina
http://linked.open...i/riv/mistoVydani	Praha
http://linked.open...i/riv/nazevZdroje	Proceedings of the 14th conference ITAT 2014 – Workshops and Posters
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...vavai/riv/projekt	Real-time detection of anomalous events in a non-stationary environment Constructing Advanced Comprehensible Classifiers
http://linked.open...UplatneniVysledku	2014
http://linked.open...iv/tvurceVysledku	Holeňa, Martin Pevný, Tomáš Kopp, Martin
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2014-09-25 (xsd:date)
number of pages	7 (xsd:int)
http://purl.org/ne...btex#hasPublisher	Ústav informatiky AV ČR
https://schema.org/isbn	978-80-87136-19-5
http://localhost/t...ganizacniJednotka	21240

Faceted Search & Find service v1.16.118 as of Jun 21 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 77 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software