Anomaly explanation with random forests

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00522404" target="_blank" >RIV/67985807:_____/20:00522404 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/20:00342500 RIV/68407700:21240/20:00342500

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.eswa.2020.113187" target="_blank" >http://dx.doi.org/10.1016/j.eswa.2020.113187</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2020.113187" target="_blank" >10.1016/j.eswa.2020.113187</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Anomaly explanation with random forests

Popis výsledku v původním jazyce

Anomaly detection has become an important topic in many domains with many different solutions proposed until now. Despite that, there are only a few anomaly detection methods trying to explain how the sample differs from the rest. This work contributes to filling this gap because knowing why a sample is considered anomalous is critical in many application domains. The proposed solution uses a specific type of random forests to extract rules explaining the difference, which are then filtered and presented to the user as a set of classification rules sharing the same consequent, or as the equivalent rule with an antecedent in a disjunctive normal form. The quality of that solution is documented by comparison with the state of the art algorithms on 34 real-world datasets.

Název v anglickém jazyce

Anomaly explanation with random forests

Popis výsledku anglicky

Anomaly detection has become an important topic in many domains with many different solutions proposed until now. Despite that, there are only a few anomaly detection methods trying to explain how the sample differs from the rest. This work contributes to filling this gap because knowing why a sample is considered anomalous is critical in many application domains. The proposed solution uses a specific type of random forests to extract rules explaining the difference, which are then filtered and presented to the user as a set of classification rules sharing the same consequent, or as the equivalent rule with an antecedent in a disjunctive normal form. The quality of that solution is documented by comparison with the state of the art algorithms on 34 real-world datasets.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

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

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Expert Systems With Applications

ISSN

0957-4174

e-ISSN

—

Svazek periodika

149

Číslo periodika v rámci svazku

1 July

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

16

Strana od-do

113187

Kód UT WoS článku

000525819400001

EID výsledku v databázi Scopus

2-s2.0-85078848410