Simplified Social Impact Theory Based Optimizer in Feature Subset Selection
Identifikátory výsledku
Kód výsledku v 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>
Výsledek na webu
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DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Simplified Social Impact Theory Based Optimizer in Feature Subset Selection
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
Simplified Social Impact Theory Based Optimizer in Feature Subset Selection
Popis výsledku anglicky
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.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
JC - Počítačový hardware a software
OECD FORD obor
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Návaznosti výsledku
Projekt
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Návaznosti
Z - Vyzkumny zamer (s odkazem do CEZ)
Ostatní
Rok uplatnění
2011
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Nature Inspired Cooperative Strategies for Optimization
ISBN
978-3-642-24093-5
ISSN
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e-ISSN
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Počet stran výsledku
15
Strana od-do
133-147
Název nakladatele
Springer
Místo vydání
Heidelberg
Místo konání akce
Cluj-Napoca
Datum konání akce
20. 10. 2011
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
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