A Contemporarymulti–Objective Feature Selection Model for Depression Detection Using a Hybrid pBGSK Optimization Algorithm
References
- Agrawal, P., Ganesh, T. and Mohamed, A. (2021). A novel binary gaining-sharing knowledge-based optimization algorithm for feature selection, Neural Computing and Applications 33: 5989–6008.
- Asim, M., Wasim, M., Sajid Ali, M. and Rehman, A. (2017). Comparison of feature selection methods in text classification on highly skewed datasets, 2017 1st International Conference on Latest trends in Electrical Engineering and Computing Technologies (INTELLECT), Karachi, Pakistan, pp. 1–8.
- Babu, N. and Kanaga, E. (2022). Sentiment analysis in social media data for depression detection using artificial intelligence: A review, SN Computer Science 3: 74.
- Burdisso, S., Errecalde, M. and Montes, M. (2019). A text classification framework for simple and effective early depression detection over social media streams, Expert Systems with Applications 133: 182–197.
- Chen, J., Huang, H., Tian, S. and Qu, Y. (2009). Feature selection for text classification with naïve Bayes, Expert Systems with Applications 36(3): 5432–5435.
- Chiong, R., Satia Budhi, G., Dhakal, S. and Chiong, F. (2021). A textual-based featuring approach for depression detection using machine learning classifiers and social media texts, Computers in Biology and Medicine 135: 104499.
- Deng, X., Li, Y., Weng, J. and Zhang, J. (2019). Feature selection for text classification: A review, Multimedia Tools and Applications 78: 3797–3816.
- Derek, A. and David, M. (2020). Support vector machine, in A. Mechelli and S. Vieira (Eds), Machine Learning, Academic Press, Chicago, pp. 101–121.
- Ding, Y., Chen, X., Fu, Q. and Zhong, S. (2020). A depression recognition method for college students using deep integrated support vector algorithm, IEEE Access 8: 75616–75629.
- Durgalakshmi, B. and Vijayakumar, V. (2020). Feature selection and classification using support vector machine and decision tree, Computational Intelligence 36: 1480–1492.
- Emary, E., Zawbaa, H. and Aboul Ella, H. (2016a). Binary ant lion approaches for feature selection, Neurocomputing 213: 54–65.
- Emary, E., Zawbaa, H.M. and Hassanien, A.E. (2016b). Binary grey wolf optimization approaches for feature selection, Neurocomputing 172: 371–381.
- Friedrich, M. (2017). Depression is the leading cause of disability around the world, Journal of the American Medical Association (JAMA) 15: 1517.
- Gao, Z., Xu, Y., Meng, F., Qi, F. and Lin, Z. (2014). Improved information gain-based feature selection for text categorization, 4th International Conference on Wireless Communication, VITAE, Aalborg, Denmark, pp. 1–5.
- Hayyolalam, V. and Kazem, A. (2020). Black widow optimization algorithm: A novel meta-heuristic approach for solving engineering optimization problems, Engineering Applications of Artificial Intelligence 87: 103249.
- Hussain, J., Satti, F., Afzal, M., Khan, W., Bilal, H., Ansaar, Z., Ahmad, H., Hur, T., Bang, J., Kim, J., Park, G., Seung, H. and Lee, S. (2019). Exploring the dominant features of social media for depression detection, Journal of Information Science 46(6): 739–759.
- Husseini Orabi, A., Buddhitha, P., Husseini Orabi, M.M. and Inkpen, D. (2018). Deep learning for depression detection of twitter users, Proceedings of the 5th Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, New Orleans, USA, pp. 88–97.
- Hussien, A.G., Oliva, D., Houssein, E.H., Juan, A.A. and Yu, X. (2020). Binary whale optimization algorithm for dimensionality reduction, Mathematics 8(10): 1821.
- Islam, M., Kabir, M., Ahmed, A., Kamal, A., Wang, H. and Ulhaq, A. (2018). Depression detection from social network data using machine learning techniques, Health Information Science and Systems 6(1): 8.
- Kowal, M., Skobel, M. and Nowicki, N. (2018). The feature selection problem in computer-assisted cytology, International Journal of Applied Mathematics and Computer Science 28(4): 759–770, DOI: 10.2478/amcs-2018-0058.
- Li, B., Yan, Q., Xu, Z. andWang, G. (2015). Weighted document frequency for feature selection in text classification, International Conference on Asian Language Processing (IALP), Suzhou, China, pp. 132–135.
- Mohamed, A., Hadi, A. and Mohamed, A. (2020). Gaining-sharing knowledge based algorithm for solving optimization problems: A novel nature-inspired algorithm, International Journal of Machine Learning and Cybernetics 11: 1501–1529.
- Moorthy, U. and Gandhi, U. (2019). Forest optimization algorithm-based feature selection using classifier ensemble, Computational Intelligence 36(4): 1445–1462.
- Moradi, P. and Gholampour, M. (2016). A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy, Applied Soft Computing 43: 117–130.
- Parlak, B. and Uysal, A. (2021). A novel filter feature selection method for text classification: Extensive feature selector, Journal of Information Science 49(1): 59–78.
- Peng, H., Long, F. and Ding, C. (2005). Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy, IEEE Transactions on Pattern Analysis and Machine Intelligence 27(8): 1226–1238.
- Połap, D. and Woźniak, M. (2021). Red fox optimization algorithm, Expert Systems with Applications 166: 114107.
- Prachi, A., Abutarboush, H., Ganesh, T. and Mohamed, A. (2021). Metaheuristic algorithms on feature selection: A survey of one decade of research (2009–2019), IEEE Access 9: 26766–26791.
- Rajalakshmi, R. and Aravindan, C. (2018). A naive Bayes approach for URL classification with supervised feature selection and rejection framework: NB for URL classification with FS and RF, Computational Intelligence 34(1): 363–396.
- Rao, R. (2016). Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems, International Journal of Industrial Engineering Computations 7: 19–34.
- Rehman, A., Javed, K. and Babri, H. (2017). Feature selection based on a normalized difference measure for text classification, Information Processing and Management 53(2): 473–489.
- Sanasam, R., Murthy, H. and Gonsalves, T. (2010). Feature selection for text classification based on Gini coefficient of inequality, Proceedings of Machine Learning Research 10: 76–85.
- Shen, J. and Rudzicz, F. (2017). Detecting anxiety through Reddit, Proceedings of the 4th Workshop on Computational Linguistics and Clinical Psychology—From Linguistic Signal to Clinical Reality, Vancouver, Canada, pp. 58–65.
- Suthaharan, S. (2016). Machine Learning Models and Algorithms for Big Data Classification, Springer, Boston, chapter “Support vector machine”, pp. 207–235.
- Tadesse, M., Lin, H., Xu, B. and Yang, L. (2019). Detection of depression-related posts in Reddit social media forum, IEEE Access 7: 44883–44893.
- Thirumoorthy, K. and Muneeswaran, K. (2020). Optimal feature subset selection using hybrid binary Jaya optimization algorithm for text classification, Sādhanā 45(201).
- Thorstad, R. and Wolff, P. (2019). Predicting future mental illness from social media: A big-data approach, Behavior Research Methods 51: 1586–1600.
- Trotzek, M., Koitka, S. and Friedrich, C. (2018). Utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences, IEEE Transactions on Knowledge and Data Engineering 32(3): 588–601.
- Unler, A., Murat, A. and Chinnam, R. (2011). MR2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification, Information Sciences 181(20): 4625–4641.
- Uysal, A. (2018). On two-stage feature selection methods for text classification, IEEE Access 6: 43233–43251.
- Wang, W., Chen, X., Musial, J. and Blazewicz, J. (2020). Two meta-heuristic algorithms for scheduling on unrelated machines with the late work criterion, International Journal of Applied Mathematics and Computer Science 30(3): 573–584, DOI: 10.34768/amcs-2020-0042.
- William, D. and Suhartono, D. (2021). Text-based depression detection on social media posts: A systematic literature review, Procedia Computer Science 179: 582–589.
- Xue, B., Zhang, M., Browne, W. and Yao, X. (2016). A survey on evolutionary computation approaches to feature selection, IEEE Transactions on Evolutionary Computation 20(4): 606–626.
- Zhu, X., Wang, Y., Li, Y., Tan, Y., Wang, G. and Song, Q. (2019). A new unsupervised feature selection algorithm using similarity-based feature clustering, Computational Intelligence 35(1): 2–22.
Language: English
Page range: 117 - 131
Submitted on: Mar 6, 2022
Accepted on: Jul 3, 2022
Published on: Mar 29, 2023
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year
Keywords:
Related subjects:
© 2023 Santhosam Kavi Priya, Kasirajan Pon Karthika, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.