Multi-Objective Gray-Wolf Optimization for Attribute Reduction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86096570" target="_blank" >RIV/61989100:27240/15:86096570 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.procs.2015.09.006" target="_blank" >http://dx.doi.org/10.1016/j.procs.2015.09.006</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.procs.2015.09.006" target="_blank" >10.1016/j.procs.2015.09.006</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multi-Objective Gray-Wolf Optimization for Attribute Reduction

Popis výsledku v původním jazyce

Feature sets are always dependent, redundant and noisy in almost all application domains. These problems in The data always declined the performance of any given classifier as it make it difficult for the training phase to converge effectively and it affect also the running time for classification at operation and training time. In this work a system for feature selection based on multi-objective gray wolf optimization is proposed. The existing methods for feature selection either depend on the data description; filter-based methods, or depend on the classifier used; wrapper approaches. These two main approaches lakes of good performance and data description in the same system. In this work gray wolf optimization; a swarm-based optimization method, wasemployed to search the space of features to find optimal feature subset that both achieve data description with minor redundancy and keeps classification performance. At the early stages of optimization gray wolf uses filter-based princi

Název v anglickém jazyce

Multi-Objective Gray-Wolf Optimization for Attribute Reduction

Popis výsledku anglicky

Feature sets are always dependent, redundant and noisy in almost all application domains. These problems in The data always declined the performance of any given classifier as it make it difficult for the training phase to converge effectively and it affect also the running time for classification at operation and training time. In this work a system for feature selection based on multi-objective gray wolf optimization is proposed. The existing methods for feature selection either depend on the data description; filter-based methods, or depend on the classifier used; wrapper approaches. These two main approaches lakes of good performance and data description in the same system. In this work gray wolf optimization; a swarm-based optimization method, wasemployed to search the space of features to find optimal feature subset that both achieve data description with minor redundancy and keeps classification performance. At the early stages of optimization gray wolf uses filter-based princi

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Procedia Computer Science. Volume 65

ISBN

—

ISSN

1877-0509

e-ISSN

—

Počet stran výsledku

10

Strana od-do

623-632

Název nakladatele

Elsevier

Místo vydání

Amsterdam

Místo konání akce

Praha

Datum konání akce

20. 4. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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