Hasib, Khan Md and Azam, Sami and Karim, Asif and Marouf, Ahmed Al and Shamrat, F. M. Javed Mehedi and Montaha, Sidratul and Yeo, Kheng Cher and Jonkman, Mirjam and Alhajj, Reda and Rokne, Jon G. (2023) MCNN-LSTM: Combining CNN and LSTM to Classify Multi-Class Text in Imbalanced News Data. IEEE Access, 11. pp. 93048-93063. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2023.3309697.
Full text not available from this repository.Abstract
Searching, retrieving, and arranging text in ever-larger document collections necessitate more efficient information processing algorithms. Document categorization is a crucial component of various information processing systems for supervised learning. As the quantity of documents grows, the performance of classic supervised classifiers has deteriorated because of the number of document categories. Assigning documents to a predetermined set of classes is called text classification. It is utilized extensively in a wide range of data-intensive applications. However, the fact that real-world implementations of these models are plagued with shortcomings begs for more investigation. Imbalanced datasets hinder the most prevalent high-performance algorithms. In this paper, we propose an approach name multi-class Convolutional Neural Network (MCNN)-Long Short-Time Memory (LSTM), which combines two deep learning techniques, Convolutional Neural Network (CNN) and Long Short-Time Memory, for text classification in news data. CNN's are used as feature extractors for the LSTMs on text input data and have the spatial structure of words in a sentence, paragraph, or document. The dataset is also imbalanced, and we use the Tomek-Link algorithm to balance the dataset and then apply our model, which shows better performance in terms of F1-score (98%) and Accuracy (99.71%) than the existing works. The combination of deep learning techniques used in our approach is ideal for the classification of imbalanced datasets with underrepresented categories. Hence, our method outperformed other machine learning algorithms in text classification by a large margin. We also compare our results with traditional machine learning algorithms in terms of imbalanced and balanced datasets.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Big data; text classification; imbalanced data; machine learning; MCNN-LSTM |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Computer System & Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 30 Oct 2025 08:02 |
| Last Modified: | 30 Oct 2025 08:02 |
| URI: | http://eprints.um.edu.my/id/eprint/49974 |
Actions (login required)
 |
View Item |
