Explaining Anomalies with Sampling Random Forests

Kód výsledku v IS VaVaI

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RIV/68407700:21240/14:00219637

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Explaining Anomalies with Sampling 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 Sampling 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

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA13-17187S" target="_blank" >GA13-17187S: Konstrukce pokročilých srozumitelných klasifikátorů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

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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