Obtaining classification rules using lvqPSO

Título:

Obtaining classification rules using lvqPSO

Autor:

Lanzarini, Laura Cristina

Colaboradores:

; Villa Monte, Augusto; Aquino, Germán; De Giusti, Armando Eduardo

Temas:

En:

Advances in Swarm and Computational Intelligence: 6th International Conference, ICSI 2015, held in conjunction with the Second BRICS Congress, CCI 2015, Beijing, China, June 25-28, 2015, Proceedings, Part I, pp. 183-193.

Resumen:

Technological advances nowadays have made it possible for processes to handle large volumes of historic information whose manual processing would be a complex task. Data mining, one of the most significant stages in the knowledge discovery and data mining (KDD) process, has a set of techniques capable of modeling and summarizing these historical data, making it easier to understand them and helping the decision making process in future situations. This article presents a new data mining adaptive technique called lvqPSO that can build, from the available information, a reduced set of simple classification rules from which the most significant relations between the features recorded can be derived. These rules operate both on numeric and nominal attributes, and they are built by combining a variation of a population metaheuristic and a competitive neural network. The method proposed was compared with several methods proposed by other authors and measured over 15 databases, and satisfactory results were obtained.

URL/DOI:

https://doi.org/10.1007/978-3-319-20466-6_20

Medio:

Soporte electrónico

Tipo de documento:

Artículo

Descripción física:

1 archivo (161,7 kB)

Idioma:

Inglés

Publicación:

, 2015

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