Multi-Objective Genetic Programming for Dataset Similarity Induction

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F15%3A10317478" target="_blank" >RIV/00216208:11320/15:10317478 - isvavai.cz</a>
Alternative codes found
RIV/67985807:_____/15:00455776
Result on the web
<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7376798" target="_blank" >http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7376798</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/SSCI.2015.222" target="_blank" >10.1109/SSCI.2015.222</a>

Result language
angličtina
Original language name
Multi-Objective Genetic Programming for Dataset Similarity Induction
Original language description
Metalearning -- the recommendation of a suitable machine learning technique for a given dataset -- relies on the concept of similarity between datasets. Traditionally, similarity measures have been constructed manually, and thus could not precisely graspthe complex relationship among the different features of the datasets. Recently, we have used an attribute alignment technique combined with genetic programming to obtain more fine-grained and trainable dataset similarity measure. In this paper, we propose an approach based on multi-objective genetic programming for evolving an attribute similarity function. Multi-objective optimization is used to encourage some of the metric properties, thus contributing to the generalization abilities of the similarity function being evolved. Experiments are performed on the data extracted from the OpenML repository and their results are compared to the baseline algorithm.
Czech name
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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
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Computational Intelligence, 2015 IEEE Symposium Series on
ISBN
978-1-4799-7560-0
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
1576-1582
Publisher name
IEEE
Place of publication
New York, USA
Event location
Kapské město, Jihoafrická republika
Event date
Dec 7, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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