Feature selection is one of the main steps in preprocessing data in machine learning, and its goal is to reduce features by removing additional and noisy features. Feature selection methods and feature reduction in a dataset must consider the accuracy of the classifying algorithms. Meta-heuristic algorithms serve as the most successful and promising methods to solve this problem. Symbiotic Organisms Search (SOS) is one of the most successful meta-heuristic algorithms inspired by organisms' interaction in nature called mutualism, commensalism, and parasitism. In this paper, three SOS-based binary approaches are offered to solve the feature selection problem. In the first and second approaches, several S-shaped transfer functions and several Chaotic Tent Function-based V-shaped transfer functions called BSOSST and BSOSVT are used to make the binary SOS (BSOS). In the third approach, an advanced BSOS based on changing SOS and the chaotic Tent function operators called EBCSOS is provided. The EBCSOS algorithm uses the chaotic Tent function and the Gaussian mutation to increase usefulness and exploration. Moreover, two new operators, i.e., BMPT and BCPT, are suggested to make the commensalism and mutualism stage binary based on a chaotic function to solve the feature selection problem. Finally, the proposed BSOSST and BSOSVT methods and the advanced version of EBCSOS were implemented on 25 datasets than the basic algorithm's binary meta-heuristic algorithms. Various experiments demonstrated that the proposed EBCSOS algorithm outperformed other methods in terms of several features and accuracy. To further confirm the proposed EBCSOS algorithm, the problem of detecting spam E-mails was applied, with the results of this experiment indicating that the proposed EBCSOS algorithm significantly improved the accuracy and speed of all categories in detecting spam E-mails.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Mafarja M et al (2019) Binary grasshopper optimisation algorithm approaches for feature selection problems. Expert Syst Appl 117:267–286
Li Y, Li T, Liu H (2017) Recent advances in feature selection and its applications. Knowl Inf Syst 53(3):551–577
Liu H, Yu L (2005) Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng 17(4):491–502
Arora S, Anand P (2019) Binary butterfly optimization approaches for feature selection. Expert Syst Appl 116:147–160
Mohammadzadeh H, Gharehchopogh FS (2020) A novel hybrid whale optimization algorithm with flower pollination algorithm for feature selection: case study E-mail spam detection. Comput Intel. https://doi.org/10.1111/coin.12397
Kabir MM, Shahjahan M, Murase K (2012) A new hybrid ant colony optimization algorithm for feature selection. Expert Syst Appl 39(3):3747–3763
Faris H et al (2018) An efficient binary salp swarm algorithm with crossover scheme for feature selection problems. Knowl-Based Syst 154:43–67
Emary E, Zawbaa HM, Hassanien AE (2016) Binary ant lion approaches for feature selection. Neurocomputing 213:54–65
Emary E, Zawbaa HM, Hassanien AE (2016) Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371–381
Mafarja MM, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302–312
Mohammadzadeh H, Gharehchopogh FS (2020) A multi-agent system based for solving high-dimensional optimization problems: a case study on E-mail spam detection. Int J Commun Syst. 34(3):e4670
Al-Madi N, Faris H, Mirjalili S (2019) Binary multi-verse optimization algorithm for global optimization and discrete problems. Int J Mach Learn Cyberne 10(12):3445–3465
Shayanfar H, Gharehchopogh FS (2018) Farmland fertility: A new metaheuristic algorithm for solving continuous optimization problems. Appl Soft Comput 71:728–746
Mirjalili S, Lewis A (2013) S-shaped versus V-shaped transfer functions for binary particle swarm optimization. Swarm Evolut Comput 9:1–14
Rodrigues D et al. (2013) BCS: A binary cuckoo search algorithm for feature selection. in 2013 IEEE International symposium on circuits and systems (ISCAS). 2013. IEEE
Mirjalili S, Mirjalili SM, Yang X-S (2014) Binary bat algorithm. Neural Comput Appl 25(3–4):663–681
Mafarja M, Mirjalili S (2018) Whale optimization approaches for wrapper feature selection. Appl Soft Comput 62:441–453
Kaur G, Arora S (2018) Chaotic whale optimization algorithm. J Comput Design Eng 5(3):275–284
Hussien AG et al (2019) S-shaped binary whale optimization algorithm for feature selection. Recent trends in signal and image processing. Springer, New york, pp 79–87
De Souza RCT et al. (2018) A V-shaped binary crow search algorithm for feature selection. In 2018 IEEE congress on evolutionary computation (CEC). IEEE
Cheng M-Y, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112
Gharehchopogh FS, Shayanfar H, Gholizadeh H (2019) A comprehensive survey on symbiotic organisms search algorithms. Artif Intell Rev 53:2265–2312
Al-Betar MA et al. (2020). Binary β-hill climbing optimizer with S-shape transfer function for feature selection. J Ambient Intell Humaniz Comput. pp 1–29
Hegazy AE, Makhlouf M, El-Tawel GS (2019) Feature selection using chaotic salp swarm algorithm for data classification. Arab J Sci Eng 44(4):3801–3816
He Y et al. (2008) A Precise Chaotic Particle Swarm Optimization Algorithm Based On Improved Tent Map. in 2008 Fourth International Conference on Natural Computation. IEEE.
Anand P, Arora S (2020) A novel chaotic selfish herd optimizer for global optimization and feature selection. Artif Intell Rev 53(2):1441–1486
Liao TW, Kuo R (2018) Five discrete symbiotic organisms search algorithms for simultaneous optimization of feature subset and neighborhood size of knn classification models. Appl Soft Comput 64:581–595
Mafarja M et al (2018) Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems. Knowl-Based Syst 145:25–45
Rajamohana S, Umamaheswari K (2018) Hybrid approach of improved binary particle swarm optimization and shuffled frog leaping for feature selection. Comput Electr Eng 67:497–508
Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Statistician 46(3):175–185
Sivanandam S, Deepa S (2008) Genetic algorithm optimization problems. In: Introduction to genetic algorithms. Springer, Berlin, Heidelberg, pp 165–209. https://doi.org/10.1007/978-3-540-73190-0_7
Zeugmann T et al. (2011) Particle swarm optimization. Encyclopedia of machine learning. pp 760–766
Yang X-S (2012) Flower Pollination Algorithm For Global Optimization. In: International conference on unconventional computing and natural computation. Springer
Sakkis G et al. (2001) Stacking classifiers for anti-spam filtering of E-mail. arXiv preprint cs/0106040
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Mohmmadzadeh, H., Gharehchopogh, F.S. An efficient binary chaotic symbiotic organisms search algorithm approaches for feature selection problems. J Supercomput 77, 9102–9144 (2021). https://doi.org/10.1007/s11227-021-03626-6
- Binary symbiotic organisms search
- Feature selection