Page 73 - IJOCTA-15-1
P. 73
BSO: Binary Sailfish Optimization for feature selection in sentiment analysis
on user preference and location in mobile envi- [23] Shadravan, S., Naji, H.R., & Bardsiri, V.K.
ronment. 2016 5th IIAI International Congress on (2019). The Sailfish Optimizer: A novel nature-
Advanced Applied Informatics (IIAI-AAI), 55–60. inspired metaheuristic algorithm for solving con-
[10] Zeng, Y., Bi, Y., Wang, J., & Lin, Y. (2015). strained engineering optimization problems. En-
Collaborative filtering recommendation algorithm gineering Applications of Artificial Intelligence,
optimization based on user attributes. 2015 8th 80, 20–34.
International Symposium on Computational In- [24] Ahmad, A., Alzaidi, K., Sari, M., & Uslu,
telligence and Design (ISCID), 1, 580–583. H. (2023). Prediction of anemia with a particle
[11] Wang, W., Wang, H.-W., & Meng, Y. (2014). swarm optimization-based approach. An Interna-
The collaborative filtering recommendation based tional Journal of Optimization and Control: The-
on sentiment analysis of online reviews. Xitong ories & Applications (IJOCTA), 13(2).
Gongcheng Lilun yu Shijian/System Engineering [25] Uzer, M.S., Yilmaz, N., & Inan, O. (2013).
Theory and Practice. Feature selection method based on artificial bee
[12] Liu, J.-p., Wang, Y., & Yan, F.-h. (2010). An im- colony algorithm and support vector machines
proved collaborative filtering recommendation al- for medical datasets classification. The Scientific
gorithm. 2010 First International Conference on World Journal, 2013(1), 419187.
Networking and Distributed Computing, 194–198. [26] Peng, H., Ying, C., Tan, S., Hu, B., & Sun, Z.
[13] Visalakshi, S., & Radha, V. (2014). A literature (2018). An improved feature selection algorithm
review of feature selection techniques and applica- based on ant colony optimization. IEEE Access,
tions: Review of feature selection in data mining. 6, 69203–69209.
2014 IEEE International Conference on Com- [27] Bektur, G., & Aslan, H.K. (2024). Artificial bee
putational Intelligence and Computing Research, colony algorithm for operating room scheduling
1–6. problem with dedicated/flexible resources and co-
[14] Jovi´c, A., Brki´c, K., & Bogunovi´c, N. (2015). operative operations. An International Journal of
A review of feature selection methods with ap- Optimization and Control: Theories & Applica-
plications. 2015 38th International Convention tions (IJOCTA), 14(3), 193–207.
on Information and Communication Technol- [28] Selvakumar, B., & Muneeswaran, K. (2019). Fire-
ogy, Electronics and Microelectronics (MIPRO), fly algorithm based feature selection for network
1200–1205. intrusion detection. Computers & Security, 81,
[15] S´anchez-Maro˜no, N., Alonso-Betanzos, A., & 148–155.
Tombilla-Sanrom´an, M. (2007). Filter methods [29] Aghdam, M.H., Ghasem-Aghaee, N., & Basiri,
for feature selection—a comparative study. In- M.E. (2009). Text feature selection using ant
ternational Conference on Intelligent Data Engi- colony optimization. Expert Systems with Appli-
neering and Automated Learning, 178–187. cations, 36(3), 6843–6853.
[16] El Aboudi, N., & Benhlima, L. (2016). Review [30] Marie-Sainte, S.L., & Alalyani, N. (2020). Fire-
on wrapper feature selection approaches. 2016 fly algorithm based feature selection for Ara-
International Conference on Engineering & MIS bic text classification. Journal of King Saud
(ICEMIS), 1–5. University-Computer and Information Sciences,
[17] Guyon, I., & Elisseeff, A. (2003). An introduc- 32(3), 320–328.
tion to variable and feature selection. Journal of [31] Basari, A.S.H., Hussin, B., Ananta, I.G.P., &
Machine Learning Research, 1157–1182. Zeniarja, J. (2013). Opinion mining of movie re-
[18] Karabo˘ga, D., & Akay, B. (2009). A comparative view using hybrid method of support vector ma-
study of artificial bee colony algorithm. Applied chine and particle swarm optimization. Procedia
Mathematics and Computation, 214(1). Engineering, 53, 453–462.
[19] Dorigo, M., Birattari, M., & St¨utzle, T. (2006). [32] Mitra, S., & Jenamani, M. (2021). Helpfulness of
Ant colony optimization. IEEE Computational online consumer reviews: A multi-perspective ap-
Intelligence Magazine, 1(4), 28–39. proach. Information Processing & Management,
[20] Kennedy, J., & Eberhart, R. (1995). Particle 58(3), 102538.
swarm optimization. Proceedings of ICNN’95- [33] Liu, D., Li, J., Du, B., Chang, J., Gao, R., &
International Conference on Neural Networks, 4, Wu, Y. (2021). A hybrid neural network approach
1942–1948. to combine textual information and rating infor-
[21] Fister, I., Fister Jr, I., Yang, X.-S., & Brest, mation for item recommendation. Knowledge and
J. (2013). A comprehensive review of firefly al- Information Systems, 63, 621–646.
gorithms. Swarm and Evolutionary Computation, [34] Linden, G., Smith, B., & York, J. (2003). Ama-
13, 34–46. zon.com recommendations: Item-to-item collabo-
[22] Mirjalili, S., & Lewis, A. (2016). The whale op- rative filtering. IEEE Internet Computing, 7(1),
timization algorithm. Advances in Engineering 76–80.
Software, 95, 51–67.
67

