Using multi-objective optimization for the selection of ensemble members
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F15%3A00231679" target="_blank" >RIV/68407700:21240/15:00231679 - isvavai.cz</a>
Result on the web
<a href="http://ceur-ws.org/Vol-1422/108.pdf" target="_blank" >http://ceur-ws.org/Vol-1422/108.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Using multi-objective optimization for the selection of ensemble members
Original language description
In this paper we propose a clustering process which uses a multi-objective evolution to select a set of diverse clusterings. The selected clusterings are then combined using a consensus method. This approach is compared to a clustering process where no selection is applied. We show that careful selection of input ensemble members can improve the overall quality of the final clustering. Our algorithm provides more stable clustering results and in many cases overcomes the limitations of base algorithms.
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
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
ITAT 2015 conference proceedings
ISBN
978-1-5151-2065-0
ISSN
1613-0073
e-ISSN
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Number of pages
7
Pages from-to
108-114
Publisher name
CEUR Workshop Proceedings
Place of publication
Aachen
Event location
Čingov
Event date
Sep 17, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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