Swarm Intelligence Based Feature Selection for High Dimensional Classification: A Literature Survey


  • Thinzar Saw Univeristy of Computer Studies, Mandalay, Myanmar
  • Phyu Hnin Myint Univeristy of Computer Studies, Mandalay, Myanmar


Feature Selection, Swarm Intelligence, High Dimensional Classification.


Feature selection is an important and challenging task in machine learning and data mining techniques to avoid the curse of dimensionality and maximize the classification accuracy. Moreover, feature selection helps to reduce computational complexity of learning algorithm, improve prediction performance, better data understanding and reduce data storage space. Swarm intelligence based feature selection approach enables to find an optimal feature subset from an extremely large dimensionality of features for building the most accurate classifier model. There is still a type of researches that is not done yet in data mining. In this paper, the utilization of swarm intelligence algorithms for feature selection process in high dimensional data focusing on medical data classification is form the subject matter. The results shows that swarm intelligence algorithms reviewed based on state-of-the-art literature have a promising capability that can be applied in feature selections techniques. The significance of this work is to present the comparison and various alternatives of swarm algorithms to be applied in feature selections for high dimensional classification.


Mustafa Serter, et. al., "Feature selection method based on artificial bee colony algorithm and support vector machines for medical datasets classification." Hindawi Publishing Corporation, The Scientific World Journal, Volume 2013, Article ID 419187, 10 pages.

Fong, Simon, et al. "Feature selection in life science classification: metaheuristic swarm search." IT Professional 16.4 (2014): 24-29.

Chen, Yiyuan et al. "An effective feature selection scheme for healthcare data classification using binary particle swarm optimization." 2018 9th International Conference on Information Technology in Medicine and Education (ITME). IEEE, 2018.

Thara, L., et al., "Swarm Intelligence Based Feature Selection Algorithms and Classifiers for Gastric Cancer Prediction." International Conference on Intelligent Data Communication Technologies and Internet of Things. Springer, Cham, 2018.

M. Dash and H. Liu, “Feature selection for classification,” Intell. Data Anal., vol. 1, nos. 1–4, pp. 131–156, 1997.

I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” J. Mach. Learn. Res., vol. 3, pp. 1157–1182, Mar. 2003.

A. S. U. Kamath, K. De Jong, and A. Shehu, “Effective automated feature construction and selection for classification of biological sequences,” PLoS One, vol. 9, no. 7, 2014, Art. ID e99982.

W. A. Albukhanajer, et al., “Evolutionary multi-objective optimization of trace transform for invariant feature extraction,” in Proc. IEEE Congr. Evol. Comput. (CEC), Brisbane, QLD, Australia, 2012, pp. 1–8.

Elbeltagi, Emad, Tarek Hegazy, and Donald Grierson. "Comparison among five evolutionary-based optimization algorithms." Advanced engineering informatics 19.1 (2005): 43-53.

Xue, Bing, et al. "A survey on evolutionary computation approaches to feature selection." IEEE Transactions on Evolutionary Computation 20.4 (2016): 606-626.

H. Liu, H. Motoda, et al., “Feature selection: An ever evolving frontier in data mining,” in Proc. JMLR Feature Sel. Data Min., vol. 10. Hyderabad, India, 2010, pp. 4–13.

H. Liu and L. Yu, “Toward integrating feature selection algorithms for classification and clustering,” IEEE Trans. Knowl. Data Eng., vol. 17, no. 4, pp. 491–502, Apr. 2005.

A. Unler and A. Murat, “A discrete particle swarm optimization method for feature selection in binary classification problems,” Eur. J. Oper. Res., vol. 206, no. 3, pp. 528–539, 2010.

Y. Liu et al., “An improved particle swarm optimization for feature selection,” J. Bionic Eng., vol. 8, no. 2, pp. 191–200, 2011.

Wang, Suhang, Jiliang Tang, and Huan Liu. "Feature selection." Encyclopedia of Machine Learning and Data Mining (2016): 1-9.

R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artificial Intelligence, vol. 97, no. 1-2, pp. 273–324, 1997.

Mashhour, Emad Mohamed, et al. "Survey on different Methods for Classifying Gene Expression using Microarray Approach." International Journal of Computer Applications 150.1 (2016).

Kennedy, James. "Swarm intelligence." in: A.Y. Zomaya (Ed.), Handbook of nature-inspired and innovative computing. Springer, Boston, MA, 2006, pp. 187-219.

Brezočnik, Lucija, et al., "Swarm Intelligence Algorithms for Feature Selection: A Review." Applied Sciences 8.9 (2018): 1521.

B. Bin, et al., "Comparison on Swarm Algorithms for Feature Selections/Reductions." International Journal of Scientific & Engineering (2014).

J. Kennedy, R. Eberhart, Particle Swarm Optimization, in: The Proceedings ofthe IEEE International Conference on Neural Networks, Washington, DC,1995, pp. 1942–1948

R. J. Mullen, D. N. Monekosso, S. A. Barman and P. wwwRemagnino, "Review: a review of ant algorithms," Expert systems with applications, vol. 36, no. 6, pp. 9608-9617, 2009.

Ismkhan, H., 2017. Effective heuristics for ant colony optimization to handle large-scale problems. Swarm and Evolutionary Computation, 32, pp.140-149.

Karaboga D. An idea based on honey bee swarm for numerical optimization. Technical Report TR06. Erciyes University. 2005.

Karaboga D. Artificial bee colony algorithm. Scholarpedia. 2010: 6915.

Zamri, et al. "Review on the Usage of Swarm Intelligence in Gene Expression Data." International Conference for Innovation in Biomedical Engineering and Life Sciences. Springer, Singapore, 2017.

Banka, H., Dara, S.: A Hamming distance based binary particle swarm optimization (HDBPSO) algorithm for high dimensional feature selection, classification and validation. Pattern Recogn. Lett. 52, 94–100 (2015)

Tran, B., Zhang, M. and Xue, B., 2016, July. A PSO based hybrid feature selection algorithm for high-dimensional classification. In Evolutionary Computation (CEC), 2016 IEEE Congress on (pp. 3801-3808).

Gupta S., Baghel, A., and Iqbal, A., 2018. Threshold Controlled Binary Particle Swarm Optimization for High Dimensional Feature Selection, International Journal of Intelligent Systems and Applications(IJISA), Vol.10, No.8, pp.75-84.

Kannan, S. Senthamarai, and N. Ramaraj. "A novel hybrid feature selection via Symmetrical Uncertainty ranking based local memetic search algorithm." Knowledge-Based Systems 23.6 (2010): 580-585.

GaneshKumar, Pugalendhi, et al. "Hybrid ant bee algorithm for fuzzy expert system based sample classification." IEEE/ACM transactions on computational biology and bioinformatics 11.2 (2014): 347-360.

Rouhi, Amirreza, and Hossein Nezamabadi-pour. "A hybrid method for dimensionality reduction in microarray data based on advanced binary ant colony algorithm." 2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC). IEEE, 2016.

M. S. Uzer et al. (Selc¸uk University) “Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification” Hindawi Publishing Corporation, The Scientific World Journal, Volume 2013, Article ID 419187, 10 pages

Alshamlan, Hala, Ghada Badr, and Yousef Alohali. "mRMR-ABC: a hybrid gene selection algorithm for cancer classification using microarray gene expression profiling." Biomed research international 2015 (2015).

Garro, Beatriz A., Katya Rodríguez, and Roberto A. Vázquez. "Classification of DNA microarrays using artificial neural networks and ABC algorithm." Applied Soft Computing 38 (2016): 548-560.

Li, Xiangtao, Mingjie Li, and Minghao Yin. "Multiobjective ranking binary artificial bee colony for gene selection problems using microarray datasets." IEEE/CAA Journal of Automatica Sinica (2016).

Liang Lan, Slobodan Vucetic,” Improving accuracy of microarray classification by a simple multi-task feature selection filter”, Int. J. Data Mining and Bioinformatics, Vol. 5, No. 2, 2011

Tran, Binh, et al. "Investigation on particle swarm optimisation for feature selection on high-dimensional data: Local search and selection bias." Connection Science 28.3 (2016): 270-294.

Kent Ridge Bio-Medical Dataset Repository, URL: http://datam.i2r.a-star.edu.sg/datasets/krbd/index.html.

UCI Machine Learning Repository, URL: http://archive.ics.uci.edu/ml/





How to Cite

Saw, T., & Hnin Myint, P. (2019). Swarm Intelligence Based Feature Selection for High Dimensional Classification: A Literature Survey. International Journal of Computer (IJC), 33(1), 69–83. Retrieved from https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1400