Feature Selection Using Hybrid Binary Grey Wolf Optimizer for Arabic Text Classification

Feature selection in Arabic text is a challenging task due to the complex and rich nature of Arabic. The feature requires solution quality, stability, conver- gence speed, ability find global optimal. This study proposes method using Hybrid Binary Gray Wolf Optimizer (HBGWO) for Ara- bic classification. HBGWO combines local search capabilities or exploratory BGWO around best solutions exploits PSO. also SCA’s finding solutions. data set used from islambook.com, which consists five Hadith books. books selected classes: Tauhid, Prayer, Zakat, Fasting, Hajj. results showed that BGWO-PSO-SCA with fitness function classification SVM could per- form better on problems. BGWO-PSO (C=1.0) gives high accuracy value 76.37% compared without selection. selec- tion provides an 88.08%. higher than other methods.