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

Muhammad Bahrul Subkhi, Chastine Fatichah, Agus Zainal Arifin

Abstract


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

Keywords


BGWO; BGWO-PSO; BGWO-PSO-SCA;Feature Selection; Text Classification

Full Text:

Full Text

References


Chantar H, Mafarja M, Alsawalqah H, Heidari AA, Aljarah I, Faris H. Feature selection using binary grey wolf optimizer with elite-based crossover for Arabic text classification. Neural Computing and Applications 2020 8;32(16):12201–12220.

Salton G, Buckley C. Term-weighting Approaches in Automatic Text Retrieval. Information Processing & Management 1988;24(5):513–523.

Singh N, Singh SB. Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance. Journal of Applied Mathematics 2017;2017.

Mirjalili S, Mirjalili SM, Lewis A. Grey Wolf Optimizer. Advances in Engineering Software 2014;69:46–61.

Kohli M, Arora S. Chaotic grey wolf optimization algorithm for constrained optimization problems. Journal of Computational Design and Engineering 2018 10;5(4):458–472.

Ghareb AS, Bakar AA, Hamdan AR. Hybrid feature selection based on enhanced genetic algorithm for text categorization. Expert Systems with Applications 2016 5;49:31–47.

Al-Tashi Q, Kadir SJA, Rais HM, Mirjalili S, Alhussian H. Binary Optimization Using Hybrid GreyWolf Optimization for Feature Selection. IEEE Access 2019;7:39496–39508.

Panwar LK, K SR, Verma A, Panigrahi BK, Kumar R. Binary Grey Wolf Optimizer for large scale unit commitment problem. Swarm and Evolutionary Computation 2018 2;38:251–266.

Singh N, Singh SB. A novel hybrid GWO-SCA approach for optimization problems. Engineering Science and Technology, an International Journal 2017 12;20(6):1586–1601.

Sahu PC, Prusty RC, Panda S. Approaching hybridized GWO-SCA based type-II fuzzy controller in AGC of diverse energy source multi area power system. Journal of King Saud University - Engineering Sciences 2020 3;32(3):186–197.

Fauzi MA, Arifin AZ, Yuniarti A. Arabic book retrieval using class and book index based term weighting. International Journal of Electrical and Computer Engineering 2017 12;7(6):3705–3710.

Emary E, Zawbaa HM, Hassanien AE. Binary grey wolf optimization approaches for feature selection. Neurocomputing 2016 1;172:371–381.

Chuang LY, Chang HW, Tu CJ, Yang CH. Improved binary PSO for feature selection using gene expression data. Computational Biology and Chemistry 2008;32(1):29–38.

Mirjalili S. SCA: A Sine Cosine Algorithm for solving optimization problems. Knowledge-Based Systems 2016 3;96:120–133.

Ariyanto ADP. Deteksi Interelasi Antar Kitab Hadis MenggunakanWord Embedding dan Ensemble Learning. PhD thesis, Master Programme in Computer Science, Institut Teknologi Sepuluh Nopember; 2022.




DOI: http://dx.doi.org/10.12962/j20882033.v33i2.13769

Refbacks

  • There are currently no refbacks.


Creative Commons License

IPTEK Journal of Science and Technology by Lembaga Penelitian dan Pengabdian kepada Masyarakat, ITS is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://iptek.its.ac.id/index.php/jts.