Risk assessment for grid computing using meta-learning ensembles

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86097026" target="_blank" >RIV/61989100:27240/15:86097026 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-17398-6_23" target="_blank" >http://dx.doi.org/10.1007/978-3-319-17398-6_23</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-17398-6_23" target="_blank" >10.1007/978-3-319-17398-6_23</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Risk assessment for grid computing using meta-learning ensembles

Popis výsledku v původním jazyce

Assessing risk associated with computational grid is an essential need for both the resource providers and the users who runs applications in grid environments. In this chapter, we modeled the prediction process of risk assessment (RA) in grid computingutilizing meta-learning approaches in order to improve the performance of the individual predictive models. In this chapter, four algorithms were selected as base classifiers, namely isotonic regression, instance base knowledge (IBK), randomizable filtered classified tree, and extra tree. Two meta-schemes, known as voting and multi schemes, were adopted to perform an ensemble risk prediction model in order to have better performance. The combination of prediction models was compared based on root mean-squared error (RMSE) to find out the best suitable algorithm. The performance of the prediction models is measured using percentage split. Experiments and assessments of these methods are performed using nine datasets for grid computing ri

Název v anglickém jazyce

Risk assessment for grid computing using meta-learning ensembles

Popis výsledku anglicky

Assessing risk associated with computational grid is an essential need for both the resource providers and the users who runs applications in grid environments. In this chapter, we modeled the prediction process of risk assessment (RA) in grid computingutilizing meta-learning approaches in order to improve the performance of the individual predictive models. In this chapter, four algorithms were selected as base classifiers, namely isotonic regression, instance base knowledge (IBK), randomizable filtered classified tree, and extra tree. Two meta-schemes, known as voting and multi schemes, were adopted to perform an ensemble risk prediction model in order to have better performance. The combination of prediction models was compared based on root mean-squared error (RMSE) to find out the best suitable algorithm. The performance of the prediction models is measured using percentage split. Experiments and assessments of these methods are performed using nine datasets for grid computing ri

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

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 355

ISBN

978-3-319-17397-9

ISSN

2194-5357

e-ISSN

—

Počet stran výsledku

10

Strana od-do

251-260

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Melaka

Datum konání akce

8. 12. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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