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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

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

Popis výsledku anglicky

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

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

<a href="/cs/project/GAP202%2F12%2F2032" target="_blank" >GAP202/12/2032: Predikce vlastností bílkovin prostorovým statistickým relačním strojovým učením</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Cluster Computing

ISSN

1386-7857

e-ISSN

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Svazek periodika

18

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

1317-1329

Kód UT WoS článku

000365236800001

EID výsledku v databázi Scopus

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