Co-evolutionary genetic programming for dataset similarity induction
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F15%3A10317487" target="_blank" >RIV/00216208:11320/15:10317487 - isvavai.cz</a>
Alternative codes found
RIV/67985807:_____/15:00459144
Result on the web
<a href="http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7257020" target="_blank" >http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7257020</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CEC.2015.7257020" target="_blank" >10.1109/CEC.2015.7257020</a>
Alternative languages
Result language
angličtina
Original language name
Co-evolutionary genetic programming for dataset similarity induction
Original language description
Metalearning deals with an important problem in machine-learning, namely selecting the right techniques to model the data at hand. In most of the metalearning approaches, a notion of similarity between datasets is needed. Our approach derives the similarity measure by combining arbitrary attribute similarity functions ordered by the optimal attribute assignment. In this paper, we propose a genetic programming based approach to the evolution of an attribute similarity inducing function. The function is composed of two parts - one describes the similarity of categorical attributes, the other describes the similarity of numerical attributes. Co-evolution is used to put these two parts together to form the similarity function. We use a repairing approach to guarantee some of the metric features for this function, and also discuss which of these features are important in metalearning.
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/GA15-19877S" target="_blank" >GA15-19877S: Automated Knowledge and Plan Modeling for Autonomous Robots</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Evolutionary Computation (CEC), 2015 IEEE Congress on
ISBN
978-1-4799-7492-4
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
1160-1166
Publisher name
IEEE
Place of publication
Neuveden
Event location
Sendai, Japonsko
Event date
May 25, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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