Azizah, Luthfia Nur and Khotimah, Purnomo Husnul and Arisal, Andria and Rozie, Andri Fachrur and Munandar, Devi and Riswantini, Dianadewi and Nugraheni, Ekasari and Suwarningsih, Wiwin and Kurniasari, Dian (2023) The Investigation into Deep Learning Classifiers Towards Imbalanced Text Data. Proceedings of the 5th International Conference on Networking, Information Systems & Security: Envisage Intelligent Systems in 5G/6G-based Interconnected Digital Worlds NISS 2022. ISSN 978-1-6654-5363-9
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Abstract
Class imbalance is an important classification problem where failure to identify events can be hazardous due to failure of solution preparation or opportune handling. Minorities are mostly more consequential in such cases. It is necessary to know a reliable classifier for imbalanced classes. This study examines several conventional machine learning and deep learning methods to compare the performance of each method on dataset with imbalanced classes. We use COVID-19 online news titles to simulate different class imbalance ratios. The results of our study demonstrate the superiority of the CNN with embedding layer method on a news titles dataset of 16,844 data points towards imbalance ratios of 37%, 30%, 20%, 10%, and 1%. However, CNN with embedding layer showed a noticeable performance degradation at an imbalance ratio of 1%. Index Terms—deep learning, classifier, online news, performance
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) > Prodi Matematika |
| Depositing User: | DIAN KURNIASARI |
| Date Deposited: | 06 Apr 2023 00:53 |
| Last Modified: | 06 Apr 2023 00:53 |
| URI: | http://repository.lppm.unila.ac.id/id/eprint/49694 |
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