Improving the Performance of an Associative Classifier by Gamma Rough Sets Based Instance Selection

Jarvin A. Antón-Vargas; Yenny Villuendas-Rey; Cornelio Yáñez-Márquez; Itzamá López-Yáñez; Oscar Camacho-Nieto

doi:10.1142/S0218001418600091

Improving the Performance of an Associative Classifier by Gamma Rough Sets Based Instance Selection

Jarvin A. Antón-Vargas, Yenny Villuendas-Rey, Cornelio Yáñez-Márquez, Itzamá López-Yáñez, Oscar Camacho-Nieto

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9 Citas (Scopus)

Resumen

This paper introduces the Gamma Rough Sets for management information systems where the universe objects are represented by continuous attributes and are connected by similarity relations. Some properties of such sets are demonstrated in this paper. In addition, Gamma Rough Sets are used to improve the Gamma associative classifier, by selecting instances. The results indicate that the selection of instances significantly reduces the computational cost of the Gamma classifier without affecting its effectiveness. The results also suggest that the selection of instances using Gamma Rough Sets favors other lazy learners, such as Nearest Neighbor and ALVOT.

Idioma original	Inglés
Número de artículo	1860009
Publicación	International Journal of Pattern Recognition and Artificial Intelligence
Volumen	32
N.º	1
DOI	https://doi.org/10.1142/S0218001418600091
Estado	Publicada - 1 ene. 2018

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Improving the Performance of an Associative Classifier by Gamma Rough Sets Based Instance Selection. / Antón-Vargas, Jarvin A.; Villuendas-Rey, Yenny ; Yáñez-Márquez, Cornelio et al.
En: International Journal of Pattern Recognition and Artificial Intelligence, Vol. 32, N.º 1, 1860009, 01.01.2018.

Producción científica: Contribución a una revista › Artículo › revisión exhaustiva

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AU - Camacho-Nieto, Oscar

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Improving the Performance of an Associative Classifier by Gamma Rough Sets Based Instance Selection

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