Risk assessment for grid computing using meta-learning ensembles
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Risk assessment for grid computing using meta-learning ensembles
Original language description
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
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Advances in Intelligent Systems and Computing. Volume 355
ISBN
978-3-319-17397-9
ISSN
2194-5357
e-ISSN
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Number of pages
10
Pages from-to
251-260
Publisher name
Springer
Place of publication
Heidelberg
Event location
Melaka
Event date
Dec 8, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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