Comparative Analysis of Feature Selection Techniques with Metaheuristic Grasshopper Optimization Algorithm
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-56728-5_14
Reference32 articles.
1. Abualigah, L.M., Khader, A.T., Al-Betar, M.A., Alyasseri, Z.A.A., Alomari, O.A., Hanandeh, E.S.: Feature selection with $$\beta $$-hill climbing search for text clustering application. In: 2017 Palestinian International Conference on Information and Communication Technology (PICICT), pp. 22–27. IEEE (2017)
2. Abukhodair, F., Alsaggaf, W., Jamal, A.T., Abdel-Khalek, S., Mansour, R.F.: An intelligent metaheuristic binary pigeon optimization-based feature selection and big data classification in a mapreduce environment. Mathematics 9(20), 2627 (2021)
3. Agrawal, P., Abutarboush, H.F., Ganesh, T., Mohamed, A.W.: Metaheuristic algorithms on feature selection: a survey of one decade of research (2009–2019). IEEE Access 9, 26766–26791 (2021)
4. Akman, D.V., et al.: K-best feature selection and ranking via stochastic approximation. Expert Syst. Appl. 213, 118864 (2023)
5. Aljarah, I., Al-Zoubi, A.M., Faris, H., Hassonah, M.A., Mirjalili, S., Saadeh, H.: Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm. Cogn. Comput. 10, 478–495 (2018)
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