TY  - JOUR
T1  - Dynamic Tabu Search for Dimensionality Reduction in Rough Set
AU - Othman, Zalinda AU - Bakar, Azuraliza Abu AU - Abdullah, Salwani AU - Ahmad Nazri, Mohd Zakree AU - Sengalang, Nelly Anak 
JO  - International Journal of Soft Computing
VL  - 8
IS  - 1
SP  - 12
EP  - 19
PY  - 2013
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2013.12.19
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2013.12.19
KW  - Tabu search
KW  -attribute reduction
KW  -rough set
KW  -computational intelligence
KW  -dynamic Tabu list
AB  - 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.
ER  -