On Scalability of Predictive Ensembles and Tradeoff Between Their Training Time and Accuracy
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
On Scalability of Predictive Ensembles and Tradeoff Between Their Training Time and Accuracy
Original language description
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.
Czech name
—
Czech description
—
Classification
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)
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Advances in Intelligent Systems and Computing II
ISBN
978-3-319-70580-4
ISSN
2194-5357
e-ISSN
—
Number of pages
13
Pages from-to
257-269
Publisher name
Springer, Cham
Place of publication
—
Event location
Lviv
Event date
Sep 5, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—