Building Predictive Models in Two Stages with Meta-Learning Templates optimized by Genetic Programming
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F14%3A00224815" target="_blank" >RIV/68407700:21240/14:00224815 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/CIEL.2014.7015740" target="_blank" >http://dx.doi.org/10.1109/CIEL.2014.7015740</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CIEL.2014.7015740" target="_blank" >10.1109/CIEL.2014.7015740</a>
Alternative languages
Result language
angličtina
Original language name
Building Predictive Models in Two Stages with Meta-Learning Templates optimized by Genetic Programming
Original language description
The model selection stage is one of the most difficult in predictive modeling. To select a model with a highest generalization performance involves benchmarking huge number of candidate models or algorithms. Often, a final model is selected without considering potentially high quality candidates just because there are too many possibilities. Improper benchmarking methodology often leads to biased estimates of model generalization performance. Automation of the model selection stage is possible, howeverthe computational complexity is huge especially when ensembles of models and optimization of input features should be also considered. In this paper we show, how to automate model selection process in a way that allows to search for complex hierarchies of ensemble models while maintaining computational tractability. We introduce two-stage learning, meta-learning templates optimized by evolutionary programming with anytime properties to be able to deliver and maintain data-tailored algori
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL) Proceedings
ISBN
978-1-4799-4513-9
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
27-34
Publisher name
IEEE
Place of publication
Piscataway
Event location
Orlando
Event date
Dec 9, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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