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International Journal of Soft Computing

ISSN: Online
ISSN: Print 1816-9503
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Dynamic Tabu Search for Dimensionality Reduction in Rough Set

Zalinda Othman, Azuraliza Abu Bakar, Salwani Abdullah, Mohd Zakree Ahmad Nazri and Nelly Anak Sengalang
Page: 12-19 | Received 21 Sep 2022, Published online: 21 Sep 2022

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Abstract

This study proposed a Dynamic Tabu Search (DTSAR) that incorporates a dynamic Tabu list to solve an attribute reduction problem in Rough Set Theory. The dynamic Tabu list is used to skip the aspiration criteria and promote faster running times. A number of experiments have been conducted to evaluate the performance of the proposed technique with other published metaheuristic techniques, rough set and decision tree. DTSAR shows promising results on reduct generation time. It ranges between 0.20-22.18 min. For comparisons on the performances that are based on the number of produced reduct, DTSAR is on par with other metaheuristic techniques. DTSAR outperforms some techniques on certain datasets. Quality of classification rules generated by adopting DTSAR is comparable with those generated by Rough Set and Decision Trees Methods.


How to cite this article:

Zalinda Othman, Azuraliza Abu Bakar, Salwani Abdullah, Mohd Zakree Ahmad Nazri and Nelly Anak Sengalang. Dynamic Tabu Search for Dimensionality Reduction in Rough Set.
DOI: https://doi.org/10.36478/ijscomp.2013.12.19
URL: https://www.makhillpublications.co/view-article/1816-9503/ijscomp.2013.12.19