Class-Based Outlier Detection: Staying Zombies or Awaiting for
Resurrection?

D 2015

Class-Based Outlier Detection: Staying Zombies or Awaiting for Resurrection?

NEZVALOVÁ, Leona, Lubomír POPELÍNSKÝ, Luis TORGO a Karel VACULÍK

Základní údaje

Originální název

Class-Based Outlier Detection: Staying Zombies or Awaiting for Resurrection?

Autoři

NEZVALOVÁ, Leona (203 Česká republika, domácí), Lubomír POPELÍNSKÝ (203 Česká republika, garant, domácí), Luis TORGO (620 Portugalsko) a Karel VACULÍK (203 Česká republika, domácí)

Vydání

Neuveden, Advances in Intelligent Data Analysis XIV - 14th International Symposium, IDA 2015, od s. 193-204, 12 s. 2015

Nakladatel

Springer

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Impakt faktor

Impact factor: 0.402 v roce 2005

Kód RIV

RIV/00216224:14330/15:00084902

Organizační jednotka

Fakulta informatiky

ISBN

978-3-319-24464-8

ISSN

DOI

http://dx.doi.org/10.1007/978-3-319-24465-5_17

UT WoS

000389228500017

Klíčová slova anglicky

class-based outlier detection; outlier interpretation; outlier description; anomaly detection; outlier detection

Štítky

core_A, firank_A

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 2. 5. 2016 06:26, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

This paper addresses the task of finding outliers within each class in the context of supervised classification problems. Class-based outliers are cases that deviate too much with respect to the cases of the same class. We introduce a novel method for outlier detection in labelled data based on Random Forests and compare it with the existing methods both on artificial and real-world data. We show that it is competitive with the existing methods and sometimes gives more intuitive results. We also provide an overview for outlier detection in labelled data. The main contribution are two methods for class-based outlier description and interpretation.

Citovat

NEZVALOVÁ, Leona, Lubomír POPELÍNSKÝ, Luis TORGO a Karel VACULÍK. Class-Based Outlier Detection: Staying Zombies or Awaiting for Resurrection?. Online. In Elisa Fromont, Tijl De Bie, Matthijs van Leeuwen. Advances in Intelligent Data Analysis XIV - 14th International Symposium, IDA 2015. Neuveden: Springer, 2015, s. 193-204. ISBN 978-3-319-24464-8. Dostupné z: https://dx.doi.org/10.1007/978-3-319-24465-5_17.

@inproceedings{1318718,
   author = {Nezvalová, Leona and Popelínský, Lubomír and Torgo, Luis and Vaculík, Karel},
   address = {Neuveden},
   booktitle = {Advances in Intelligent Data Analysis XIV - 14th International Symposium, IDA 2015},
   doi = {http://dx.doi.org/10.1007/978-3-319-24465-5_17},
   editor = {Elisa Fromont, Tijl De Bie, Matthijs van Leeuwen},
   keywords = {class-based outlier detection; outlier interpretation; outlier description; anomaly detection; outlier detection},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Neuveden},
   isbn = {978-3-319-24464-8},
   pages = {193-204},
   publisher = {Springer},
   title = {Class-Based Outlier Detection: Staying Zombies or Awaiting for Resurrection?},
   year = {2015}
}

TY  - JOUR
ID  - 1318718
AU  - Nezvalová, Leona - Popelínský, Lubomír - Torgo, Luis - Vaculík, Karel
PY  - 2015
TI  - Class-Based Outlier Detection: Staying Zombies or Awaiting for Resurrection?
PB  - Springer
CY  - Neuveden
SN  - 9783319244648
KW  - class-based outlier detection
KW  - outlier interpretation
KW  - outlier description
KW  - anomaly detection
KW  - outlier detection
N2  - This paper addresses the task of finding outliers within each class in the context of supervised classification problems. Class-based outliers are cases that deviate too much with respect to the cases of the same class. We introduce a novel method for outlier detection in labelled data based on Random Forests and compare it with the existing methods both on artificial and real-world data. We show that it is competitive with the existing methods and sometimes gives more intuitive results. We also provide an overview for outlier detection in labelled data. The main contribution are two methods for class-based outlier description and interpretation.
ER  -

NEZVALOVÁ, Leona, Lubomír POPELÍNSKÝ, Luis TORGO a Karel VACULÍK. Class-Based Outlier Detection: Staying Zombies or Awaiting for Resurrection?. Online. In Elisa Fromont, Tijl De Bie, Matthijs van Leeuwen. \textit{Advances in Intelligent Data Analysis XIV - 14th International Symposium, IDA 2015}. Neuveden: Springer, 2015, s.~193-204. ISBN~978-3-319-24464-8. Dostupné z: https://dx.doi.org/10.1007/978-3-319-24465-5\_{}17.

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