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

The result's identifiers

  • Result code in 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>

  • Alternative codes found

    RIV/61989100:27740/16:86099958

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

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

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    R - Projekt Ramcoveho programu EK

Others

  • Publication year

    2016

  • 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 424

  • ISBN

    978-3-319-28030-1

  • ISSN

    2194-5357

  • e-ISSN

  • Number of pages

    11

  • Pages from-to

    151-161

  • Publisher name

    Springer Verlag

  • Place of publication

    London

  • Event location

    Kochi

  • Event date

    Dec 16, 2015

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article