Abstract
In this digital era, the amount of data generated by various functions has increased dramatically with each row and column; this has a negative impact on analytics and will increase the liability of computer algorithms that are used for pattern recognition. Dimensionality reduction (DR) techniques may be used to address the issue of dimensionality. It will be addressed by using two methods: feature extraction (FE) and feature selection (FS). This article focuses on the study of feature selection algorithms, which includes static data. However, with the advent of Web-based applications and IoT, the data are generated with dynamic features and inflate at a rapid rate, thus it is prone to possess noisy data, which further limits the algorithm’s efficiency. The scalability of the FS strategies is endangered as the size of the data collection increases. As a result, the existing DR methods do not address the issues with dynamic data. The utilization of FS methods not only reduces the load of the data, but it also avoids the issues associated with overfitting.
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References
B. Venkatesh, J. Anuradha, A review of feature selection and its methods. Cybern. Inf. Technol. 19, 3–26 (2019)
I.A. Gheyas, L.S. Smith, Feature subset selection in large dimensionality domains. Pattern Recogn. 43, 5–13 (2010)
V.R. Balasaraswathi, M. Sugumaran, Y. Hamid, Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms. J. Commun. Inf. Netw. 2, 107–119 (2017)
A. Varun, M. Sandeep Kumar, A comprehensive review of the pigeon-inspired optimization algorithm. Int. J. Eng. Technol. 7 (2018). www.sciencepubco.com/index.php/IJET
S. Colaco, S. Kumar, A. Tamang, V.G. Biju, A review on feature selection algorithms, in Advances in Intelligent Systems and Computing, vol. 906 (Springer Verlag, 2019), pp. 133–153
X.-S. Yang, Nature-inspired mateheuristic algorithms: success and new challenges. J. Comput. Eng. Inf. Technol. 1 (2012)
X.S. Yang, S. Koziel, Computational optimization: an overview. Stud. Comput. Intell. 356, 1–11 (2011)
X.-S. Yang, Firefly algorithm, stochastic test functions and design optimisation. Int. J. Bio-Inspired Comput. 2, 78–84 (2010)
X.S. Yang, S. Deb, Cuckoo search via Lévy flights, in 2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009—Proceedings (2009), pp. 210–214. https://doi.org/10.1109/NABIC.2009.5393690.
M.C. Su, S.Y. Su, Y.X. Zhao, A swarm-inspired projection algorithm. Pattern Recogn. 42, 2764–2786 (2009)
X.S. Yang, Bat algorithm: literature review and applications. Int. J. Bio-Inspired Comput. 5, 141–149 (2013)
H. Williams, M. Bishop, Stochastic diffusion search: a comparison of swarm intelligence parameter estimation algorithms with RANSAC. Algorithms 7, 206–228 (2014)
S. Maroufpoor, R. Azadnia, O. Bozorg-Haddad, Stochastic optimization: Stochastic diffusion search algorithm, in Handbook of Probabilistic Models (Elsevier, 2019), pp. 437–448. https://doi.org/10.1016/B978-0-12-816514-0.00017-5
M.M. al-Rifaie, J.M. Bishop, Stochastic diffusion search review. Paladyn J. Behav. Robot. 4 (2015)
T. Niknam, S.I. Taheri, J. Aghaei, S. Tabatabaei, M. Nayeripour, A modified honey bee mating optimization algorithm for multiobjective placement of renewable energy resources. Appl. Energ. 88, 4817–4830 (2011)
W.T. Pan, A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowledge-Based Syst. 26, 69–74 (2012)
M. Talo, O. Yildirim, U.B. Baloglu, G. Aydin, U.R. Acharya, Convolutional neural networks for multi-class brain disease detection using MRI images. Comput. Med. Imaging Graph. 78, 101673 (2019)
B. Niu, H. Wang, Bacterial colony optimization. Discret. Dyn. Nat. Soc. 2012 (2012)
E. Duman, M. Uysal, A.F. Alkaya, Migrating birds optimization: a new meta-heuristic approach and its application to the quadratic assignment problem, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6624 LNCS (2011), pp. 254–263
A.H. Gandomi, A.H. Alavi, Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17, 4831–4845 (2012)
C. Sur, A. Shukla, Green heron swarm optimization algorithm-state-of-the-art of a new nature inspired discrete meta-heuristics
X. Meng, Y Liu, X. Gao, H. Zhang, A new bio-inspired algorithm: Chicken swarm optimization. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) 8794, 86–94 (2014)
(6) (PDF) Dispersive Flies Optimisation. https://www.researchgate.net/publication/267514160_Dispersive_Flies_Optimisation
S. Mirjalili, The ant lion optimizer. Adv. Eng. Softw. 83, 80–98 (2015)
G.G. Wang, S. Deb, L.D.S. Coelho, Elephant herding optimization, in Proceedings—2015 3rd International Symposium on Computational and Business Intelligence, ISCBI 2015 (Institute of Electrical and Electronics Engineers Inc., 2016), pp. 1–5. https://doi.org/10.1109/ISCBI.2015.8
H. Liu, G. Xu, G.Y. Ding, Y.B. Sun, Human behavior-based particle swarm optimization. Sci. World J. 2014 (2014)
M. Naghdiani, M. Jahanshahi, GSO: a new solution for solving unconstrained optimization tasks using garter snake’s behavior, in Proceedings—2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017 (Institute of Electrical and Electronics Engineers Inc., 2018), pp. 328–333. https://doi.org/10.1109/CSCI.2017.55
E. Hosseini, Laying chicken algorithm: a new meta-heuristic approach to solve continuous programming problems (2017).https://doi.org/10.4172/2168-9679.1000344
J. Zhang, M. Xiao, L. Gao, Q. Pan, Queuing search algorithm: a novel metaheuristic algorithm for solving engineering optimization problems. Appl. Math. Model. 63, 464–490 (2018)
S. Arora, S. Singh, Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput. 23, 715–734 (2019)
S. Harifi, M. Khalilian, J. Mohammadzadeh, S. Ebrahimnejad, Emperor Penguins Colony: a new metaheuristic algorithm for optimization. Evol. Intell. 12, 211–226 (2019)
J.B. Lamy, Artificial feeding birds (AFB): a new metaheuristic inspired by the behavior of pigeons, in EAI/Springer Innovations in Communication and Computing (Springer Science and Business Media Deutschland GmbH, 2019), pp. 43–60. https://doi.org/10.1007/978-3-319-96451-5_3
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Mahalakshmi, D., Balamurugan, S.A.A., Chinnadurai, M., Vaishnavi, D. (2022). Nature-Inspired Feature Selection Algorithms: A Study. In: Karrupusamy, P., Balas, V.E., Shi, Y. (eds) Sustainable Communication Networks and Application. Lecture Notes on Data Engineering and Communications Technologies, vol 93. Springer, Singapore. https://doi.org/10.1007/978-981-16-6605-6_55
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