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

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

Abstract

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.

Original language	English
Article number	1860009
Journal	International Journal of Pattern Recognition and Artificial Intelligence
Volume	32
Issue number	1
DOIs	https://doi.org/10.1142/S0218001418600091
State	Published - 1 Jan 2018

Keywords

Gamma rough sets
associative classifiers
instance selection
lazy learners

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10.1142/S0218001418600091

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abstract = "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.",

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AU - López-Yáñez, Itzamá

AU - Camacho-Nieto, Oscar

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AB - 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.

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KW - lazy learners

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ER -

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

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Keywords

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