Simplified Social Impact Theory Based Optimizer in Feature Subset Selection

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F11%3A00186612" target="_blank" >RIV/68407700:21230/11:00186612 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Simplified Social Impact Theory Based Optimizer in Feature Subset Selection
Original language description
The interactions taking place in the society could be a source of rich inspiration for the development of novel computational methods. This paper describes an application of two optimization methods based on the idea of social interactions. The first oneis the Social Impact Theory based Optimizer - a novel method directly inspired by and based on the Dynamic Theory of Social Impact known from social psychology. The second one is the binary Particle Swarm Optimization - well known optimization technique, which could be understood as to be inspired by decision making process in a group. The two binary optimization methods are applied in the area of automatic pattern classification to selection of an optimal subset of classifier's inputs. The testing isperformed using four datasets from UCI repository. The results show the ability of both methods to significantly reduce input dimensionality and simultaneously keep up the generalization ability.
Czech name
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Czech description
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Publication year
2011
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Nature Inspired Cooperative Strategies for Optimization
ISBN
978-3-642-24093-5
ISSN
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e-ISSN
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Number of pages
15
Pages from-to
133-147
Publisher name
Springer
Place of publication
Heidelberg
Event location
Cluj-Napoca
Event date
Oct 20, 2011
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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