Scalability of predictive ensembles

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00328801" target="_blank" >RIV/68407700:21230/17:00328801 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21240/17:00328801
Result on the web
<a href="http://toc.proceedings.com/36770webtoc.pdf" target="_blank" >http://toc.proceedings.com/36770webtoc.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/STC-CSIT.2017.8098848" target="_blank" >10.1109/STC-CSIT.2017.8098848</a>

Result language
angličtina
Original language name
Scalability of predictive ensembles
Original language description
Recent meta-learning approaches are oriented towards algorithm selection, optimization or recommendation of existing algorithms. In this paper we show how inductive algorithms constructed from building blocks on small data subsample can be scaled up to model large data sets. We demonstrate how one particular template (simple ensemble of fast sigmoidal regression models) outperforms state-of-the-art approaches on the Airline data set.
Czech name
—
Czech description
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Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT)
ISBN
978-1-5386-1639-0
ISSN
—
e-ISSN
—
Number of pages
6
Pages from-to
555-560
Publisher name
IEEE (Institute of Electrical and Electronics Engineers)
Place of publication
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Event location
Lvov
Event date
Sep 5, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000425922100126

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