Selekce přízkové podmnožiny založená na sociálním dopadu

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F08%3A03146412" target="_blank" >RIV/68407700:21230/08:03146412 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Social Impact based Approach to 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

Social Impact based Approach to 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.

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2008

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Nature Inspired Cooperative Strategies for Optimization (NICSO 2007)

ISBN

978-3-540-78986-4

ISSN

1860-949X

e-ISSN

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Počet stran výsledku

10

Strana od-do

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Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Acireale

Datum konání akce

8. 11. 2007

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000262048100022