Explaining Anomalies with Sapling Random Forests

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00219644" target="_blank" >RIV/68407700:21230/14:00219644 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985807:_____/14:00432430

Výsledek na webu

<a href="http://www.library.sk/i2/content.csg.cls?ictx=cav&repo=crepo1&key=70202320092" target="_blank" >http://www.library.sk/i2/content.csg.cls?ictx=cav&repo=crepo1&key=70202320092</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Explaining Anomalies with Sapling Random Forests

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Explaining Anomalies with Sapling Random Forests

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 14th conference ITAT 2014 ? Workshops and Posters

ISBN

978-80-87136-19-5

ISSN

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e-ISSN

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Počet stran výsledku

8

Strana od-do

71-78

Název nakladatele

Institute of Computer Science AS CR

Místo vydání

Praha

Místo konání akce

Demänovská Dolina

Datum konání akce

25. 9. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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