Anomaly explanation with random forests
The result's identifiers
Result code in 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>
Alternative codes found
RIV/68407700:21230/20:00342500 RIV/68407700:21240/20:00342500
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Anomaly explanation with random forests
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Expert Systems With Applications
ISSN
0957-4174
e-ISSN
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Volume of the periodical
149
Issue of the periodical within the volume
1 July
Country of publishing house
GB - UNITED KINGDOM
Number of pages
16
Pages from-to
113187
UT code for WoS article
000525819400001
EID of the result in the Scopus database
2-s2.0-85078848410