On Scalability of Predictive Ensembles and Tradeoff Between Their Training Time and Accuracy

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F18%3A00315809" target="_blank" >RIV/68407700:21240/18:00315809 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-319-70581-1_18" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-70581-1_18</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-70581-1_18" target="_blank" >10.1007/978-3-319-70581-1_18</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On Scalability of Predictive Ensembles and Tradeoff Between Their Training Time and Accuracy

Popis výsledku v původním jazyce

Scalability of predictive models is often realized by data subsampling. The generalization performance of models is not the only criterion one should take into account in the algorithm selection stage. For many real world applications, predictive models have to be scalable and their training time should be in balance with their performance. For many tasks it is reasonable to save computational resources and select an algorithm with slightly lower performance and significantly lower training time. In this contribution we made extensive benchmarks of predictive algorithms scalability and examined how they are capable to trade accuracy for lower training time. We demonstrate how one particular template (simple ensemble of fast sigmoidal regression models) outperforms state-of-the-art approaches on the Airline data set.

Název v anglickém jazyce

On Scalability of Predictive Ensembles and Tradeoff Between Their Training Time and Accuracy

Popis výsledku anglicky

Scalability of predictive models is often realized by data subsampling. The generalization performance of models is not the only criterion one should take into account in the algorithm selection stage. For many real world applications, predictive models have to be scalable and their training time should be in balance with their performance. For many tasks it is reasonable to save computational resources and select an algorithm with slightly lower performance and significantly lower training time. In this contribution we made extensive benchmarks of predictive algorithms scalability and examined how they are capable to trade accuracy for lower training time. We demonstrate how one particular template (simple ensemble of fast sigmoidal regression models) outperforms state-of-the-art approaches on the Airline data set.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Intelligent Systems and Computing II

ISBN

978-3-319-70580-4

ISSN

2194-5357

e-ISSN

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Počet stran výsledku

13

Strana od-do

257-269

Název nakladatele

Springer, Cham

Místo vydání

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Místo konání akce

Lviv

Datum konání akce

5. 9. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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