Asynchronous Evolution of Data Mining Workflow Schemes by Strongly Typed Genetic Programming

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F16%3A10333214" target="_blank" >RIV/00216208:11320/16:10333214 - isvavai.cz</a>
Result on the web
<a href="http://ieeexplore.ieee.org/abstract/document/7814654/" target="_blank" >http://ieeexplore.ieee.org/abstract/document/7814654/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICTAI.2016.0094" target="_blank" >10.1109/ICTAI.2016.0094</a>

Result language
angličtina
Original language name
Asynchronous Evolution of Data Mining Workflow Schemes by Strongly Typed Genetic Programming
Original language description
This paper describes an algorithm for the automated design of whole machine learning workflows, including preprocessing of the data and automatic creation of several types of ensembles. The algorithm is based on strongly typed genetic programming which ensures the validity of the workflows. The evolution of the individuals in the population is asynchronous in order to improve the utilization of computational resources. The approach is validated on four data sets from the UCI machine learning repository.
Czech name
—
Czech description
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Project
<a href="/en/project/GA15-19877S" target="_blank" >GA15-19877S: Automated Knowledge and Plan Modeling for Autonomous Robots</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Tools with Artificial Intelligence (ICTAI), 2016 IEEE 28th International Conference on
ISBN
978-1-5090-4460-3
ISSN
2375-0197
e-ISSN
—
Number of pages
8
Pages from-to
577-584
Publisher name
IEEE
Place of publication
New York
Event location
San Jose
Event date
Nov 6, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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