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Dimensionality Reduction Using Hybrid Brainstorm Optimization Algorithm

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Proceedings of International Conference on Data Science and Applications

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 287))

Abstract

In this work, a swarm intelligence-based algorithm, brainstorm optimization, is proposed for reducing dimensionality (feature selection) in datasets that are used for classification. Dimensionality reduction is a well-known and widely used technique in analyzing big data. Its role is to reduce the number of features in high-dimensional datasets and to keep only those that contain useful and rich information. This results in better understanding and interpretation of data, higher accuracy, and boosting the training process of machine learning method used for classification. After extracting features from the dataset, it should be decided which subset of features will be used in the training process. Since, in high-dimensional datasets many features exist, this problem is categorized as NP hard and it is necessary to employ metaheuristics for its solving. For tackling this issue, a binary hybrid brainstorm optimization metaheuristics that overcome the drawbacks of original algorithm, is proposed. For performance evaluation, 21 datasets are used. The comparative analysis is made between the proposed approach and the original brainstorm optimization algorithm, as well as with nine other metaheuristics adopted for feature selection. Experimental results prove the robustness of proposed method, since it is capable to reduce the number of features by simultaneously achieving better classification accuracy than other methods taken for comparative analysis.

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References

  1. Van Der Maaten, L., Postma, E., Van den Herik, J.: Dimensionality reduction: a comparative. J. Mach. Learn. Res. 10(66–71), 13 (2009)

    Google Scholar 

  2. Trunk, G.V.: A problem of dimensionality: a simple example. IEEE Trans. Pattern Anal. Mach. Intell. 3, 306–307 (1979)

    Article  Google Scholar 

  3. Marimont, R., Shapiro, M.: Nearest neighbor searches and the curse of dimensionality. IMA J. Appl. Math. 24(1), 59–70 (1979)

    Article  Google Scholar 

  4. Levine, M.D.: Feature extraction: a survey. Proc. IEEE 57(8), 1391–1407 (1969)

    Article  Google Scholar 

  5. Abdi, H., Williams, L.J.: Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2(4), 433–459 (2010)

    Article  Google Scholar 

  6. Bunte, K., Haase, S., Biehl, M., Villmann, T.: Stochastic neighbor embedding (SNE) for dimension reduction and visualization using arbitrary divergences. Neurocomputing 90, 23–45 (2012)

    Article  Google Scholar 

  7. Izenman, A.J.: Linear discriminant analysis. In: Modern Multivariate Statistical Techniques, pp. 237–280, Springer (2013)

    Google Scholar 

  8. Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40(1), 16–28 (2014)

    Article  Google Scholar 

  9. Johnson, D.S.: The np-completeness column: an ongoing guide. J. Algorithms 6(3), 434–451 (1985)

    Article  MathSciNet  Google Scholar 

  10. Karaboga, D., Basturk, B.: Artificial bee colony (abc) optimization algorithm for solving constrained optimization problems. In: International Fuzzy Systems Association World Congress, pp. 789–798, Springer (2007)

    Google Scholar 

  11. Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 65–74. Springer (2010)

    Google Scholar 

  12. Heidari, A.A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., Chen, H.: Harris hawks optimization: algorithm and applications. Futur. Gener. Comput. Syst. 97, 849–872 (2019)

    Article  Google Scholar 

  13. Zivkovic, M., Bacanin, N., Tuba, E., Strumberger, I., Bezdan, T., Tuba, M.: Wireless sensor networks life time optimization based on the improved firefly algorithm. In: 2020 International Wireless Communications and Mobile Computing (IWCMC), pp. 1176–1181. IEEE (2020)

    Google Scholar 

  14. Bacanin, N., Tuba, E., Zivkovic, M., Strumberger, I., Tuba, M.: Whale optimization algorithm with exploratory move for wireless sensor networks localization. In: International Conference on Hybrid Intelligent Systems, pp. 328–338. Springer (2019)

    Google Scholar 

  15. Zivkovic, M., Bacanin, N., Zivkovic, T., Strumberger, I., Tuba, E., Tuba, M.: Enhanced grey wolf algorithm for energy efficient wireless sensor networks. In: 2020 Zooming Innovation in Consumer Technologies Conference (ZINC), pp. 87–92. IEEE (2020)

    Google Scholar 

  16. Bezdan, T., Zivkovic, M., Tuba, E., Strumberger, I., Bacanin, N., Tuba, M.: Multi-objective task scheduling in cloud computing environment by hybridized bat algorithm. In: International Conference on Intelligent and Fuzzy Systems, pp. 718–725, Springer (2020)

    Google Scholar 

  17. Bacanin, N., Bezdan, T., Tuba, E., Strumberger, I., Tuba, M., Zivkovic, M.: Task scheduling in cloud computing environment by grey wolf optimizer. In: 2019 27th Telecommunications Forum (TELFOR), pp. 1–4. IEEE (2019)

    Google Scholar 

  18. Nebojsa Bacanin, E.T.I.S., Bezdan, T., Tuba, M.: Optimizing convolutional neural network hyperparameters by enhanced swarm intelligence metaheuristics. Algorithms 13(3), 67 (2020)

    Google Scholar 

  19. Bacanin, N., Bezdan, T., Tuba, E., Strumberger, I., Tuba, M.: Monarch butterfly optimization based convolutional neural network design. Mathematics 8(6), 936 (2020)

    Article  Google Scholar 

  20. Bezdan, T., Tuba, E., Strumberger, I., Bacanin, N., Tuba, M.: Automatically designing convolutional neural network architecture with artificial flora algorithm. In: Tuba, M., Akashe, S., Joshi, A., (eds) ICT Systems and Sustainability (Singapore), pp. 371–378. Springer Singapore (2020)

    Google Scholar 

  21. Strumberger, I., Tuba, E., Bacanin, N., Zivkovic, M., Beko, M., Tuba, M.: Designing convolutional neural network architecture by the firefly algorithm. In: 2019 International Young Engineers Forum (YEF-ECE), pp. 59–65. IEEE (2019)

    Google Scholar 

  22. Zivkovic, M., Bacanin, N., Venkatachalam, K., Nayyar, A., Djordjevic, A., Strumberger, I., Al-Turjman, F.: Covid-19 cases prediction by using hybrid machine learning and beetle antennae search approach. Sustainable Cities and Society 66, 102669 (2021)

    Article  Google Scholar 

  23. Bezdan, T., Zivkovic, M., Tuba, E., Strumberger, I., Bacanin, N., Tuba, M.: Glioma brain tumor grade classification from mri using convolutional neural networks designed by modified fa. In: International Conference on Intelligent and Fuzzy Systems, pp. 955–963. Springer (2020)

    Google Scholar 

  24. Bansal, J.C., Sharma, H., Jadon, S.S., Clerc, M.: Spider monkey optimization algorithm for numerical optimization. Memetic Comput. 6(1), 31–47 (2014)

    Article  Google Scholar 

  25. Strumberger, I., Bacanin, N., Tuba, M.: Enhanced firefly algorithm for constrained numerical optimization. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 2120–2127. IEEE (2017)

    Google Scholar 

  26. Shi, Y.: Brain storm optimization algorithm. In: International Conference in Swarm Intelligence, pp. 303–309. Springer (2011)

    Google Scholar 

  27. Liang, J., Runarsson, T.P., Mezura-Montes, E., Clerc, M., Suganthan, P., Coello, C., Deb, K.: Problem deflnitions and evaluation criteria for the cec 2006 special session on constrained real-parameter optimization (2006)

    Google Scholar 

  28. Strumberger, I., Bacanin, N., Tuba, M.: Enhanced firefly algorithm for constrained numerical optimization, ieee congress on evolutionary computation. In: Proceedings of the IEEE International Congress on Evolutionary Computation (CEC 2017), pp. 2120–2127 (2017)

    Google Scholar 

  29. Tuba, M., Bacanin, N.: Improved seeker optimization algorithm hybridized with firefly algorithm for constrained optimization problems. Neurocomputing 143, 197–207 (2014)

    Article  Google Scholar 

  30. Bacanin, N., Tuba, M.: Firefly algorithm for cardinality constrained mean-variance portfolio optimization problem with entropy diversity constraint. The Scientific World Journal, Special Issue Computational Intelligence and Metaheuristic Algorithms with Applications, vol. 2014, no. Article ID 721521, p. 16 (2014)

    Google Scholar 

  31. Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) Stochastic Algorithms: Foundations and Applications, (Berlin, Heidelberg), pp. 169–178. Springer, Berlin Heidelberg (2009)

    Chapter  Google Scholar 

  32. Xue, B., Zhang, M., Browne, W.N.: Particle swarm optimization for feature selection in classification: novel initialization and updating mechanisms. Appl. Soft Comput. 18, 261–276 (2014)

    Article  Google Scholar 

  33. Hussien, A.G., Oliva, D., Houssein, E.H., Juan, A.A., Yu, X.: Binary whale optimization algorithm for dimensionality reduction. Mathematics 8(10) (2020)

    Google Scholar 

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Acknowledgements

The paper is supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia, Grant No. III-44006.

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Authors and Affiliations

  1. Singidunum University, Danijelova 32, 11000, Belgrade, Serbia

    Nebojsa Bacanin, Miodrag Zivkovic, Timea Bezdan, Dusan Cvetnic & Luka Gajic

Authors
  1. Nebojsa Bacanin
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  2. Miodrag Zivkovic
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  3. Timea Bezdan
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  4. Dusan Cvetnic
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  5. Luka Gajic
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Corresponding author

Correspondence to Nebojsa Bacanin .

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Editors and Affiliations

  1. Computer Science & Engineering and Information Technology, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India

    Mukesh Saraswat

  2. Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India

    Sarbani Roy

  3. Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India

    Chandreyee Chowdhury

  4. Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia

    Amir H. Gandomi

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Bacanin, N., Zivkovic, M., Bezdan, T., Cvetnic, D., Gajic, L. (2022). Dimensionality Reduction Using Hybrid Brainstorm Optimization Algorithm. In: Saraswat, M., Roy, S., Chowdhury, C., Gandomi, A.H. (eds) Proceedings of International Conference on Data Science and Applications. Lecture Notes in Networks and Systems, vol 287. Springer, Singapore. https://doi.org/10.1007/978-981-16-5348-3_54

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