Alqablan, Tamara Amjad Abdelkarim (2025) Enhanced Gray Wolf Algorithm Based Feature Selection For Sentiment Analysis. PhD thesis, Universiti Sains Malaysia.
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Abstract
People access social media platforms through a range of IoT devices, including smartphones, tablets, and wearable devices, which lead to the generation of a significant quantity of data. The high dimensional of data is considered a major challenge in analyzing and extracting knowledge from big data. Feature selection is a technique concerned with selecting only the most relevant features to improve performance. In the context of wrapper-based feature selection approaches, the algorithm is often stuck in a poor solution (feature subset) due to its inability to efficiently explore the search space. To tackle this problem, first, an enhanced wrapper-based approach that combines the BGWO algorithm with the AβHC local search algorithm (Aβ-BGWO) was proposed to enhance the BGWO exploitation capabilities and improve its performance in terms of local search ability. Second, enhance the Aβ-BGWO by integrating it with BHHO (Aβ-BGWHHO) to improve the global convergence of A
| Item Type: | Thesis (PhD) |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science |
| Divisions: | Pusat Pengajian Sains Komputer (School of Computer Sciences) > Thesis |
| Depositing User: | Mr Aizat Asmawi Abdul Rahim |
| Date Deposited: | 30 Sep 2026 09:33 |
| Last Modified: | 30 Sep 2026 09:33 |
| URI: | http://eprints.usm.my/id/eprint/65070 |
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