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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

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

verfasst von : Pavel Kordík, Tomáš Frýda

Erschienen in: Advances in Intelligent Systems and Computing II

Verlag: Springer International Publishing

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Abstract

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.

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Metadaten
Titel
On Scalability of Predictive Ensembles and Tradeoff Between Their Training Time and Accuracy
verfasst von
Pavel Kordík
Tomáš Frýda
Copyright-Jahr
2018
Buch
Advances in Intelligent Systems and Computing II

Print ISBN: 978-3-319-70580-4

Electronic ISBN: 978-3-319-70581-1

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-70581-1_18

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