Ensemble Learning of Runtime Prediction Models for Gene-expression Analysis Workflows

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00233473" target="_blank" >RIV/68407700:21230/15:00233473 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/article/10.1007/s10586-015-0481-5" target="_blank" >http://link.springer.com/article/10.1007/s10586-015-0481-5</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10586-015-0481-5" target="_blank" >10.1007/s10586-015-0481-5</a>

Result language
angličtina
Original language name
Ensemble Learning of Runtime Prediction Models for Gene-expression Analysis Workflows
Original language description
The adequate management of scientific workflow applications strongly depends on the availability of accurate performance models of sub-tasks. Numerous approaches use machine learning to generate such models autonomously, thus alleviating the human effortassociated to this process. However, these standalone models may lack robustness, leading to a decay on the quality of information provided to workflow systems on top. This paper presents a novel approach for learning ensemble prediction models of tasksruntime. The ensemble-learning method entitled bootstrap aggregating (bagging) is used to produce robust ensembles of M5P regression trees of better predictive performance than could be achieved by standalone models. Our approach has been tested on geneexpression analysis workflows. The results show that the ensemble method leads to significant prediction-error reductions when compared with learned standalone models. This is the first initiative using ensemble learning for generating p
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JC - Computer hardware and software
OECD FORD branch
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Project
<a href="/en/project/GAP202%2F12%2F2032" target="_blank" >GAP202/12/2032: Predicting protein properties through spatial statistical relational machine learning</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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