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A Contemporarymulti–Objective Feature Selection Model for Depression Detection Using a Hybrid pBGSK Optimization Algorithm

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International Journal of Applied Mathematics and Computer Science
Image Analysis, Classification and Protection (Special section, pp. 7-70), Marcin Niemiec, Andrzej Dziech and Jakob Wassermann (Eds.)
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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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

Deng, X., Li, Y., Weng, J. and Zhang, J. (2019). Feature selection for text classification: A review, Multimedia Tools and Applications 78: 3797–3816. Search in Google Scholar

Derek, A. and David, M. (2020). Support vector machine, in A. Mechelli and S. Vieira (Eds), Machine Learning, Academic Press, Chicago, pp. 101–121. Search in Google Scholar

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. Search in Google Scholar

Durgalakshmi, B. and Vijayakumar, V. (2020). Feature selection and classification using support vector machine and decision tree, Computational Intelligence 36: 1480–1492. Search in Google Scholar

Emary, E., Zawbaa, H. and Aboul Ella, H. (2016a). Binary ant lion approaches for feature selection, Neurocomputing 213: 54–65. Search in Google Scholar

Emary, E., Zawbaa, H.M. and Hassanien, A.E. (2016b). Binary grey wolf optimization approaches for feature selection, Neurocomputing 172: 371–381. Search in Google Scholar

Friedrich, M. (2017). Depression is the leading cause of disability around the world, Journal of the American Medical Association (JAMA) 15: 1517. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Open DOISearch in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

Moorthy, U. and Gandhi, U. (2019). Forest optimization algorithm-based feature selection using classifier ensemble, Computational Intelligence 36(4): 1445–1462. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

Połap, D. and Woźniak, M. (2021). Red fox optimization algorithm, Expert Systems with Applications 166: 114107. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

Suthaharan, S. (2016). Machine Learning Models and Algorithms for Big Data Classification, Springer, Boston, chapter “Support vector machine”, pp. 207–235. Search in Google Scholar

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. Search in Google Scholar

Thirumoorthy, K. and Muneeswaran, K. (2020). Optimal feature subset selection using hybrid binary Jaya optimization algorithm for text classification, Sādhanā 45(201). Search in Google Scholar

Thorstad, R. and Wolff, P. (2019). Predicting future mental illness from social media: A big-data approach, Behavior Research Methods 51: 1586–1600. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

Uysal, A. (2018). On two-stage feature selection methods for text classification, IEEE Access 6: 43233–43251. Search in Google Scholar

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. Open DOISearch in Google Scholar

William, D. and Suhartono, D. (2021). Text-based depression detection on social media posts: A systematic literature review, Procedia Computer Science 179: 582–589. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

eISSN:
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Langue:
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Sujets de la revue:
Mathematics, Applied Mathematics