Influence of Type and Level of Noise on the Performance of an Adaptive Novelty Detector

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F17%3A00317623" target="_blank" >RIV/68407700:21220/17:00317623 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.computer.org/csdl/proceedings/icci*cc/2017/0771/00/08109776.pdf" target="_blank" >https://www.computer.org/csdl/proceedings/icci*cc/2017/0771/00/08109776.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICCI-CC.2017.8109776" target="_blank" >10.1109/ICCI-CC.2017.8109776</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Influence of Type and Level of Noise on the Performance of an Adaptive Novelty Detector

Popis výsledku v původním jazyce

This paper investigates the influence of the signal to noise ratio (SNR) and the type of a noise on the performance of two adaptive novelty detection methods. The evaluated methods are Learning Entropy (LE) and Error and Learning Based Novelty Detection (ELBND). The methods are compared in empirical way in classification framework. A classification based only on the error of the adaptive model was used as a reference. The research in this field is important, because a noise is present in every measured data and can drastically influence the result of tasks like the novelty detection. Moreover, various types of noise can influence the novelty detection in different ways, therefore the optimal method of adaptive novelty detection can be hard to choose. This assumption is supported by experimental results in this study.

Název v anglickém jazyce

Influence of Type and Level of Noise on the Performance of an Adaptive Novelty Detector

Popis výsledku anglicky

This paper investigates the influence of the signal to noise ratio (SNR) and the type of a noise on the performance of two adaptive novelty detection methods. The evaluated methods are Learning Entropy (LE) and Error and Learning Based Novelty Detection (ELBND). The methods are compared in empirical way in classification framework. A classification based only on the error of the adaptive model was used as a reference. The research in this field is important, because a noise is present in every measured data and can drastically influence the result of tasks like the novelty detection. Moreover, various types of noise can influence the novelty detection in different ways, therefore the optimal method of adaptive novelty detection can be hard to choose. This assumption is supported by experimental results in this study.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

2017 IEEE 16TH INTERNATIONAL CONFERENCE ON COGNITIVE INFORMATICS & COGNITIVE COMPUTING (ICCI*CC)

ISBN

9781538607701

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

373-377

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Oxford

Datum konání akce

26. 7. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000426941300058