ADVANCED TECHNIQUES IN MULTI-LABEL TEXT CLASSIFICATION: INTEGRATION OF BETA ANT COLONY AND DEEP LEARNING APPROACHES
DOI:
https://doi.org/10.29121/shodhkosh.v5.i7.2024.2243Keywords:
Multi-Label Text Classification, Beta Ant Colony Optimization (Baco), Deep Learning, Feature Selection, Hybrid Algorithms, Text MiningAbstract [English]
The rapid growth of unstructured textual data in various domains has necessitated the development of sophisticated techniques for multi-label text classification. Traditional methods often struggle with handling the complexity and interdependence of multiple labels, leading to suboptimal performance. This paper presents an advanced approach that integrates the Beta Ant Colony Optimization (BACO) algorithm with deep learning techniques for multi-label text classification. The BACO algorithm effectively explores the feature space and selects relevant features by leveraging pheromone trails, while the deep learning model captures intricate patterns and relationships within the textual data. The integration of these two methodologies aims to enhance the efficiency and accuracy of multi-label classification tasks, particularly in domains where label dependency is prominent. Empirical evaluations on benchmark datasets demonstrate that the proposed hybrid approach outperforms existing state-of-the-art techniques in terms of precision, recall, F1-score, and computational efficiency. The findings suggest that combining heuristic optimization algorithms with deep learning can significantly improve multi-label text classification performance, providing a robust solution for real-world applications.
References
Zhang, M.-L., & Zhou, Z.-H. (2014). A Review on Multi-Label Learning Algorithms. IEEE Transactions on Knowledge and Data Engineering, 26(8), 1819-1837.
Tsoumakas, G., Katakis, I., & Vlahavas, I. (2010). Mining Multi-Label Data. In Data Mining and Knowledge Discovery Handbook (pp. 667-685). Springer. DOI: https://doi.org/10.1007/978-0-387-09823-4_34
Chou, S.-H., Hsieh, H.-H., & Lin, Y.-C. (2020). Multi-label Text Classification Based on Attention Mechanism and Convolutional Neural Networks. Information Sciences, 515, 16-28.
Kaur, P., & Kumar, D. (2018). Ant Colony Optimization: A Technique for Multi-label Classification. International Journal of Computer Applications, 179(17), 29-33.
Liu, J., & Wu, Y. (2021). A Deep Neural Network Approach to Multi-Label Text Classification. Knowledge-Based Systems, 211, 106544. DOI: https://doi.org/10.1016/j.knosys.2020.106544
Kocev, D., Vens, C., Struyf, J., & Džeroski, S. (2007). Ensembles of Multi-Label Decision Trees. Proceedings of the European Conference on Machine Learning, 250-257.
Sun, Y., Li, C., & Zhu, J. (2020). Multi-label Text Classification with Capsule Network. Knowledge-Based Systems, 188, 105041.
Yang, Y., & Pedersen, J. O. (1997). A Comparative Study on Feature Selection in Text Categorization. Proceedings of the 14th International Conference on Machine Learning, 412-420.
Li, Z., Liu, Z., & Zhao, H. (2019). Feature Selection Using Hybrid Ant Colony Optimization Algorithm for Multi-Label Classification. Neural Computing and Applications, 31(5), 1465-1480.
Nam, J., Kim, J., Mencía, E. L., Gurevych, I., & Fürnkranz, J. (2014). Large-Scale Multi-label Text Classification – Revisiting Neural Networks. Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 437-452. DOI: https://doi.org/10.1007/978-3-662-44851-9_28
Shrivastava, A., Chakkaravarthy, M., Shah, M.A..A Novel Approach Using Learning Algorithm for Parkinson’s Disease Detection with Handwritten Sketches. In Cybernetics and Systems, 2022 DOI: https://doi.org/10.1080/01969722.2022.2157599
Shrivastava, A., Chakkaravarthy, M., Shah, M.A., A new machine learning method for predicting systolic and diastolic blood pressure using clinical characteristics. In Healthcare Analytics, 2023, 4, 100219 DOI: https://doi.org/10.1016/j.health.2023.100219
Shrivastava, A., Chakkaravarthy, M., Shah, M.A.,Health Monitoring based Cognitive IoT using Fast Machine Learning Technique. In International Journal of Intelligent Systems and Applications in Engineering, 2023, 11(6s), pp. 720–729
Shrivastava, A., Rajput, N., Rajesh, P., Swarnalatha, S.R., IoT-Based Label Distribution Learning Mechanism for Autism Spectrum Disorder for Healthcare Application. In Practical Artificial Intelligence for Internet of Medical Things: Emerging Trends, Issues, and Challenges, 2023, pp. 305–321 DOI: https://doi.org/10.1201/9781003315476-16
Boina, R., Ganage, D., Chincholkar, Y.D., .Chinthamu, N., Shrivastava, A., Enhancing Intelligence Diagnostic Accuracy Based on Machine Learning Disease Classification. In International Journal of Intelligent Systems and Applications in Engineering, 2023, 11(6s), pp. 765–774
Shrivastava, A., Pundir, S., Sharma, A., ...Kumar, R., Khan, A.K. Control of A Virtual System with Hand Gestures. In Proceedings - 2023 3rd International Conference on Pervasive Computing and Social Networking, ICPCSNShrivastava, A., Pundir, S., Sharma, A., ...Kumar, R., Khan, A.K. Control of A Virtual System with Hand Gestures. In Proceedings - 2023 3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023, 2023, pp. 1716–1721 DOI: https://doi.org/10.1109/ICPCSN58827.2023.00288
Read, J., Pfahringer, B., Holmes, G., & Frank, E. (2011). Classifier Chains for Multi-label Classification. Machine Learning, 85(3), 333-359. DOI: https://doi.org/10.1007/s10994-011-5256-5
Chorowski, J., Bahdanau, D., Serdyuk, D., Cho, K., & Bengio, Y. (2015). Attention-Based Models for Speech Recognition. Advances in Neural Information Processing Systems, 28, 577-585.
Huang, C., & Liu, C. (2019). Hybrid Deep Learning Approach for Multi-label Text Classification. Proceedings of the International Joint Conference on Neural Networks (IJCNN), 1-6.
Zhang, Y., & Zhou, Z.-H. (2014). A Review on Multi-Label Learning Algorithms with Applications. IEEE Transactions on Knowledge and Data Engineering, 26(8), 1819-1837. DOI: https://doi.org/10.1109/TKDE.2013.39
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