Mendeley
TY  _  JOUR
ID  -  1394090912521669
TI  -  Introducing a New Version of Binary Ant Colony Algorithm to Solve the Problem of Feature Selection
JO  -  Nashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iran
JA  -  ES
LA  -  fa
SN  -  16823745
AU  -  Kashef S.
AU  -  Nezamabadi-pour H.
AD  -  دانشگاه شهید باهنر کرمان
AD  -  دانشگاه شهید باهنر کرمان
Y1  -  2015
PY  -  2015
VL  -  4
IS  -  
SP  -  127
EP  -  134
KW  -  Feature selection
wrapper
Ant colony optimization (ACO)
binary ACO
classification
DO  -  
N2  -  The use of metaheuristic algorithms is a good choice for solving optimization problems. In this paper, a novel feature selection algorithm based on Ant Colony Optimization (ACO), called Advanced Binary ACO (ABACO), is presented. This algorithm is an advanced version of binary ant colony optimization, which attempts to solve the problems of ACO and BACO algorithms by combination of these two. The performance of proposed algorithm is compared to the performance of Binary Genetic Algorithm (BGA), Binary Particle Swarm Optimization (BPSO), and some prominent ACO-based algorithms on the task of feature selection on 12 well-known UCI datasets. Simulation results verify that the algorithm provides a suitable feature subset with good classification accuracy using a smaller feature set than competing feature selection methods.
UR  - rimag.ir/en/Article/28102
L1  - rimag.ir/en/Article/Download/28102
ER  -  
