Discovering predictive ensembles for transfer learning and meta-learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F18%3A00328802" target="_blank" >RIV/68407700:21240/18:00328802 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s10994-017-5682-0" target="_blank" >https://link.springer.com/article/10.1007/s10994-017-5682-0</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10994-017-5682-0" target="_blank" >10.1007/s10994-017-5682-0</a>
Alternative languages
Result language
angličtina
Original language name
Discovering predictive ensembles for transfer learning and meta-learning
Original language description
Recent meta-learning approaches are oriented towards algorithm selection, optimization or recommendation of existing algorithms. In this article we show how data-tailored algorithms can be constructed from building blocks on small data sub-samples. Building blocks, typically weak learners, are optimized and evolved into data-tailored hierarchical ensembles. Good-performing algorithms discovered by evolutionary algorithm can be reused on data sets of comparable complexity. Furthermore, these algorithms 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. Evolved hierarchical ensembles can therefore be beneficial as algorithmic building blocks in meta-learning, including meta-learning at scale.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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
Name of the periodical
Machine Learning
ISSN
0885-6125
e-ISSN
1573-0565
Volume of the periodical
107
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
31
Pages from-to
177-207
UT code for WoS article
000419684700007
EID of the result in the Scopus database
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