Ensemble of flexible neural trees for predicting risk in grid computing environment

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099958" target="_blank" >RIV/61989100:27240/16:86099958 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/16:86099958

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007/978-3-319-28031-8_13" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-319-28031-8_13</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-28031-8_13" target="_blank" >10.1007/978-3-319-28031-8_13</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Ensemble of flexible neural trees for predicting risk in grid computing environment

Popis výsledku v původním jazyce

Risk assessment in grid computing is an important issue as grid is a shared environment with diverse resources spread across several administrative domains. Therefore, by assessing risk in grid computing, we can analyze possible risks for the growing consumption of computational resources of an organization and thus we can improve the organization's computation effectiveness. In this paper, we used a function approximation tool, namely, flexible neural tree for risk prediction and risk (factors) identification. Flexible neural tree is a feed forward neural network model, where network architecture was evolved like a tree. Our comprehensive experiment finds score for each risk factor in grid computing together with a general tree-based model for predicting risk. We used an ensemble of prediction models to achieve generalization. (C) Springer International Publishing Switzerland 2016.

Název v anglickém jazyce

Ensemble of flexible neural trees for predicting risk in grid computing environment

Popis výsledku anglicky

Risk assessment in grid computing is an important issue as grid is a shared environment with diverse resources spread across several administrative domains. Therefore, by assessing risk in grid computing, we can analyze possible risks for the growing consumption of computational resources of an organization and thus we can improve the organization's computation effectiveness. In this paper, we used a function approximation tool, namely, flexible neural tree for risk prediction and risk (factors) identification. Flexible neural tree is a feed forward neural network model, where network architecture was evolved like a tree. Our comprehensive experiment finds score for each risk factor in grid computing together with a general tree-based model for predicting risk. We used an ensemble of prediction models to achieve generalization. (C) Springer International Publishing Switzerland 2016.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

R - Projekt Ramcoveho programu EK

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Intelligent Systems and Computing. Volume 424

ISBN

978-3-319-28030-1

ISSN

2194-5357

e-ISSN

—

Počet stran výsledku

11

Strana od-do

151-161

Název nakladatele

Springer Verlag

Místo vydání

London

Místo konání akce

Kochi

Datum konání akce

16. 12. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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