Two new feature selection algorithms with rough sets theory

Rough Sets Theory has opened new trends for the development of the Incomplete Information Theory. Inside this one, the notion of reduct is a very significant one, but to obtain a reduct in a decision system is an expensive computing process although very important in data analysis and knowledge disc...

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Detalles Bibliográficos
Autores principales: Caballero, Yailé, Bello, Rafael, Álvarez, Delia, García Lorenzo, María Matilde
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2006
Materias:
Ciencias Informáticas
Algorithms
genetic algorithm
estimation of distribution algorithms
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/23903
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-23903
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Algorithms
genetic algorithm
estimation of distribution algorithms
spellingShingle Ciencias Informáticas
Algorithms
genetic algorithm
estimation of distribution algorithms
Caballero, Yailé
Bello, Rafael
Álvarez, Delia
García Lorenzo, María Matilde
Two new feature selection algorithms with rough sets theory
topic_facet Ciencias Informáticas
Algorithms
genetic algorithm
estimation of distribution algorithms
description Rough Sets Theory has opened new trends for the development of the Incomplete Information Theory. Inside this one, the notion of reduct is a very significant one, but to obtain a reduct in a decision system is an expensive computing process although very important in data analysis and knowledge discovery. Because of this, it has been necessary the development of different variants to calculate reducts. The present work look into the utility that offers Rough Sets Model and Information Theory in feature selection and a new method is presented with the purpose of calculate a good reduct. This new method consists of a greedy algorithm that uses heuristics to work out a good reduct in acceptable times. In this paper we propose other method to find good reducts, this method combines elements of Genetic Algorithm with Estimation of Distribution Algorithms. The new methods are compared with others which are implemented inside Pattern Recognition and Ant Colony Optimization Algorithms and the results of the statistical tests are shown.
format Objeto de conferencia
Objeto de conferencia
author Caballero, Yailé
Bello, Rafael
Álvarez, Delia
García Lorenzo, María Matilde
author_facet Caballero, Yailé
Bello, Rafael
Álvarez, Delia
García Lorenzo, María Matilde
author_sort Caballero, Yailé
title Two new feature selection algorithms with rough sets theory
title_short Two new feature selection algorithms with rough sets theory
title_full Two new feature selection algorithms with rough sets theory
title_fullStr Two new feature selection algorithms with rough sets theory
title_full_unstemmed Two new feature selection algorithms with rough sets theory
title_sort two new feature selection algorithms with rough sets theory
publishDate 2006
url http://sedici.unlp.edu.ar/handle/10915/23903
work_keys_str_mv AT caballeroyaile twonewfeatureselectionalgorithmswithroughsetstheory
AT bellorafael twonewfeatureselectionalgorithmswithroughsetstheory
AT alvarezdelia twonewfeatureselectionalgorithmswithroughsetstheory
AT garcialorenzomariamatilde twonewfeatureselectionalgorithmswithroughsetstheory
bdutipo_str Repositorios
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