TY  - JOUR
EP  - 114958
SN  - 21693536
PB  - Institute of Electrical and Electronics Engineers Inc.
N1  - cited By 0
TI  - Population Initialization Factor in Binary Multi-Objective Grey Wolf Optimization for Features Selection
SP  - 114942
AV  - none
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141507067&doi=10.1109%2fACCESS.2022.3218056&partnerID=40&md5=869ce35d61e6247411a36c5a794b6e3e
A1  - Albashah, N.L.S.
A1  - Rais, H.M.
JF  - IEEE Access
VL  - 10
Y1  - 2022///
N2  - Features selection methods not only reduce the dimensionality, but also improve significantly the classification results. In this study, the effect of the initialization population using the population factor has been explored. There are twenty wolves obtained by the population initialization method in binary multi-objective grey wolf optimization for features selection. There are two objectives function that will be minimized i.e. number of features and error rate. The proposed method has been compared with the previous study Binary Multi-Objective Grey Wolf Optimization (BMOGWO-S) using UCI datasets, oil and gas datasets. The results reflect that the proposed method outperforms all existence methods in terms of reducing feature numbers and error rates. © 2013 IEEE.
KW  - Feature Selection; Multiobjective optimization
KW  -  Classification results; Error rate; Feature selection methods; Features selection; Gray wolf optimizer; Gray wolves; Multi objective; Optimisations; Optimizers; Population initializations
KW  -  Classification (of information)
ID  - scholars17466
ER  -