Ensemble of Flexible Neural Trees for Predicting Risk in Grid Computing Environment

  • Sara AbdelwahabEmail author
  • Varun Kumar OjhaEmail author
  • Ajith AbrahamEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 424)


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.


Risk assessment Flexible neural tree Feature selection Grid computing 



This work was supported by the IPROCOM Marie Curie Initial Training Network, funded through the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007–2013/, under REA grant agreement number 316555.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologySudan University of Science and TechnologyKhartoumSudan
  2. 2.Computer Science and Information CollegePrincess Norah Bint Abddulrahman UniversityRiyadhSaudi Arabia
  3. 3.IT4InnovationsVSB Technical University of OstravaOstravaCzech Republic
  4. 4.Machine Intelligence Research Labs (MIR Labs)WashingtonUSA

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