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Título: Two new feature selection algorithms with rough sets theory
Autores: Caballero, Yailé
Bello, Rafael
Álvarez, Delia
García Lorenzo, María Matilde
Fecha: 2012-11-08
2006-08
2006-08
Publicador: Unversidad Nacional de La Plata
Fuente:

Tipo: Objeto de conferencia
Objeto de conferencia
Tema: genetic algorithm
estimation of distribution algorithms
Algorithms
Ciencias Informáticas
Descripción: 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.
IFIP International Conference on Artificial Intelligence in Theory and Practice - Knowledge Acquisition and Data Mining
Idioma: Inglés