Evolutionary Optimization of Meta Data Metric for Method Recommendation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F13%3A10286095" target="_blank" >RIV/00216208:11320/13:10286095 - isvavai.cz</a>
Alternative codes found
RIV/67985807:_____/13:00425750
Result on the web
<a href="http://dx.doi.org/10.1109/ICCIS.2013.6751590" target="_blank" >http://dx.doi.org/10.1109/ICCIS.2013.6751590</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICCIS.2013.6751590" target="_blank" >10.1109/ICCIS.2013.6751590</a>
Alternative languages
Result language
angličtina
Original language name
Evolutionary Optimization of Meta Data Metric for Method Recommendation
Original language description
Metalearning - a method for recommendation the most suitable data-mining algorithm to an unknown dataset - is an important problem that needs to be solved in order to design a completely autonomous data-mining solver. This paper deals with this particular problem by proposing a machinelearning method which recommends the most suitable algorithm to an unknown dataset based on the results of previous data-mining experiments. The fundamental idea behind this is that the algorithms will perform similarly onsimilar datasets. The choice of datasets features - called meta data - is presented and the metric comparing datasets is optimized by means of evolutionary computation.
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
<a href="/en/project/GAP202%2F11%2F1368" target="_blank" >GAP202/11/1368: Learning of functional relationships from high-dimensional data</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 2013 IEEE Conference on Cybernetics and Intelligent Systems, (CIS)
ISBN
978-1-4799-1072-4
ISSN
2326-8123
e-ISSN
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Number of pages
5
Pages from-to
123-127
Publisher name
IEEE Computer Society
Place of publication
Manilla
Event location
Manila; Philippines
Event date
Nov 12, 2013
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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