International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Special issue: The 10th International Conference on Awareness Science and Technology (iCAST 2019)

Ashis Kumar Mandal1*, Rikta Sen1, Basabi Chakraborty2

1 Graduate School of Software & Information Science, Iwate Prefectural University, Iwate, Japan
2 Faculty of Software & Information Science, Iwate Prefectural University, Iwate, Japan

 

Download Citation: |
Download PDF


ABSTRACT

Owl Search Algorithm (OSA) is a recently proposed nature-inspired meta-heuristic algorithm which is easily implementable and exhibits great potential for solving continuous optimization problems. In our earlier work, a binary version of owl search algorithm (BOSA), with transfer functions for mapping the continuous solution space into a binary one, has been developed and applied in optimal feature subset selection problem. In our preliminary simulation experiments, it was found that the performance of the solution depends on the type of transfer function used. In this work, an extensive analysis of various types of transfer functions and their respective effects on the selection of optimal feature subset has been studied by simulation experiments with multiple benchmark datasets. Transfer functions of three different families, S-shaped, V-shaped and quadratic, are used for designing eleven BOSA models, each of which is made by combining individual transfer function. The performances of the proposed wrapper based feature subset selection algorithm based on several BOSA models have been evaluated by simulation experiments with twenty datasets for finding out the best model. The best observed BOSA model has also been compared with other similar meta-heuristics algorithms for feature subset selection. Experimental results show that the feature subset selected by BOSA with quadratic transfer function produces the highest classification accuracy with the minimum number of selected features compared to other algorithms.


Keywords: Binary owl search algorithm; Meta-heuristics; Feature selection; Transfer functions.


Share this article with your colleagues

 


REFERENCES

  1. Aghdam, M.H., Ghasem-Aghaee, N., Basiri, M.E. 2009. Text feature selection using ant colony optimization. Expert Systems with Applications, 36, part 2, 6843–6853.

  2. Allam, M. Nandhini, M. 2018. Optimal feature selection using binary teaching learning based optimization algorithm. Journal of King Saud University - Computer and Information Sciences. doi:10.1016/j.jksuci. 2018. 12.001.

  3. Al-Tashi, Q., Abdul Kadir, S.J., Rais, H.M., Mirjalili, S., Alhussian, H. 2019. Binary optimization using hybrid grey wolf optimization for feature selection. IEEE Access, 7, 39496–39508.

  4. Arora, S., Anand, P. 2019. Binary butterfly optimization approaches for feature selection. Expert Systems with Applications, 116, 147–160.

  5. Banka, H., Dara, S. 2015. A hamming distance based binary particle swarm optimization (HDBPSO) algorithm for high dimensional feature selection, classification and validation. Pattern Recognition Letters, 52, 94–100.

  6. BAŞ, E., ÜLKER, E. 2020. An efficient binary social spider algorithm for feature selection problem. Expert Systems with Applications, 146, 113185.

  7. Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A. 2015. Recent advances and emerging challenges of feature selection in the context of big data. Knowledge-Based Systems, 86, 33–45.

  8. Cao, Y., Wang, Q., Wang, Z., Jermsittiparsert, K., Shafiee, M. 2020. A new optimized configuration for capacity and operation improvement of CCHP system based on developed owl search algorithm. Energy Reports, 6, 315–324.

  9. Chakraborty, B. 2014. Rough fuzzy consistency measure with evolutionary algorithm for attribute reduction. In 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 723–728.

  10. Chandrashekar, G., Sahin, F. 2014. A survey on feature selection methods. Computers and Electrical Engineering, 40, 16–28.

  11. Chuang, L.Y., Tsai, S.W., Yang, C.H. 2011. Improved binary particle swarm optimization using catfish effect for feature selection. Expert Systems with Applications, 38, 12699–12707.

  12. Crawford, B., Soto, R., Astorga, G., García, J., Castro, C., Paredes, F. 2017. Putting continuous metaheuristics to work in binary search spaces. Complexity, ed. Hao, J., 2017, 8404231.

  13. Dash, M., Liu, H. 2003. Consistency-based search in feature selection. Artificial Intelligence, 151, 155–176.

  14. Dash, R. 2018. An adaptive harmony search approach for gene selection and classification of high dimensional medical data. Journal of King Saud University - Computer and Information Sciences. doi:10.1016/j.jksuci. 2018.02.013.

  15. Dua, D., Graff, C. 2019. UCI machine learning repository, 2017. URL http://archive.ics.uci.edu/ml, 37.

  16. El-Ashmawi, W.H., Abd Elminaam, D.S., Nabil, A.M., Eldesouky, E. 2020. A chaotic owl search algorithm based bilateral negotiation model. Ain Shams Engineering Journal. doi:10.1016/j.asej. 2020.01.005.

  17. Emary, E., Zawbaa, H.M., Hassanien, A.E. 2016. Binary ant lion approaches for feature selection. Neurocomputing, 213, 54–65.

  18. Emary, E., Zawbaa, H.M., Hassanien, A.E. 2016. Binary grey wolf optimization approaches for feature selection. Neurocomputing, 172, 371–381.

  19. Engelbrecht, A.P., Pampara, G. 2007. Binary differential evolution strategies. In 2007 IEEE Congress on Evolutionary Computation, 1942–1947.

  20. Ewees, A.A., El Aziz, M.A., Hassanien, A.E. 2019. Chaotic multi-verse optimizer-based feature selection. Neural Computing and Applications, 31, 991–1006.

  21. Farhan, A.F., Feilat, E.A., Al-Salaymeh, A.S. 2019. Maximum power point tracking technique using combined perturb & observe and owl search algorithms. In 2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019, 4–8. doi:10.1109/ICECTA48151.2019.8959738.

  22. Faris, H., Mafarja, M.M., Heidari, A.A., Aljarah, I., Al-Zoubi, A.M., Mirjalili, S., Fujita, H. 2018. An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems. Knowledge-Based Systems, 154, 43–67.

  23. Fausto, F., Reyna-Orta, A., Cuevas, E., Andrade, Á.G., Perez-Cisneros, M. 2019. From ants to whales: metaheuristics for all tastes. Artificial Intelligence Review, Springer Netherlands. doi:10.1007/s10462-018-09676-2.

  24. Guha, R., Ghosh, M., Kapri, S., Shaw, S., Mutsuddi, S., Bhateja, V., Sarkar, R. 2019. Deluge based Genetic Algorithm for feature selection. Evolutionary Intelligence. doi:10.1007/s12065-019-00218-5.

  25. Han, C., Zhou, G., Zhou, Y. 2019. Binary Symbiotic Organism Search Algorithm for Feature Selection and Analysis. IEEE Access, 7, 166833–166859.

  26. Inbarani, H.H., Bagyamathi, M., Azar, A.T. 2015. A novel hybrid feature selection method based on rough set and improved harmony search. Neural Computing and Applications, 26, 1859–1880.

  27. Jain, A.K., Murty, M.N., Flynn, P.J. 1999. Data clustering: a review. ACM computing surveys (CSUR), 31, 264–323.

  28. Jain, M., Maurya, S., Rani, A., Singh, V. 2018. Owl search algorithm: A novel nature-inspired heuristic paradigm for global optimization. Journal of Intelligent and Fuzzy Systems, 34, 1573–1582.

  29. Jordehi, A.R. 2019. Binary particle swarm optimization with quadratic transfer function: A new binary optimization algorithm for optimal scheduling of appliances in smart homes. Applied Soft Computing, 78, 465–480.

  30. Kabir, M.M., Shahjahan, M., Murase, K. 2012. A new hybrid ant colony optimization algorithm for feature selection. Expert Systems with Applications, 39, 3747–3763.

  31. Kennedy, J., Eberhart, R.C. 1997. A discrete binary version of the particle swarm algorithm. In 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, 4104–4108.

  32. Khushaba, R.N., Al-Ani, A., Al-Jumaily, A. 2011. Feature subset selection using differential evolution and a statistical repair mechanism. Expert Systems with Applications, 38, 11515–11526.

  33. Kumar, U.S., Inbarani, H.H. 2017. PSO-based feature selection and neighborhood rough set-based classification for BCI multiclass motor imagery task. Neural Computing and Applications, 28, 3239–3258.

  34. Li, D., Deng, L., Su, Q., Song, Y. 2020. Providing a guaranteed power for the BTS in telecom tower based on improved balanced owl search algorithm. Energy Reports, 6, 297–307.

  35. Liu, Y., Wang, G., Chen, H., Dong, H., Zhu, X., Wang, S. 2011. An improved particle swarm optimization for feature selection. Journal of Bionic Engineering, 8, 191–200.

  36. Lu, H., Chen, J., Yan, K., Jin, Q., Xue, Y., Gao, Z. 2017. A hybrid feature selection algorithm for gene expression data classification. Neurocomputing, 256, 56–62.

  37. Mafarja, M., Aljarah, I., Faris, H., Hammouri, A.I., Al-Zoubi, A.M., Mirjalili, S. 2019. Binary grasshopper optimization algorithm approaches for feature selection problems. Expert Systems with Applications, 117, 267–286.

  38. Mafarja, M., Aljarah, I., Heidari, A.A., Faris, H., Fournier-Viger, P., Li, X., Mirjalili, S. 2018. Binary dragonfly optimization for feature selection using time-varying transfer functions. Knowledge-Based Systems, 161, August, 185–204.

  39. Mafarja, M., Mirjalili, S. 2018. Whale optimization approaches for wrapper feature selection. Applied Soft Computing Journal, 62, 441–453.

  40. Mafarja, M.M., Mirjalili, S. 2017. Hybrid Whale Optimization Algorithm with simulated annealing for feature selection. Neurocomputing, 260, 302–312.

  41. Mandal, A.K., Sen, R., Chakraborty, B. 2019. Binary owl search algorithm for feature subset selection. In 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST), 1–6. doi:10.1109/ ICAwST.2019.8923486.

  42. Manoj, R.J., Anto Praveena, M.D., Vijayakumar, K. 2019. An ACO–ANN based feature selection algorithm for big data. Cluster Computing, 22, 3953–3960.

  43. Mirjalili, S., Lewis, A. 2013. S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization. Swarm and Evolutionary Computation, 9, 1–14.

  44. Mistry, K., Zhang, L., Neoh, S.C., Lim, C.P., Fielding, B. 2017. A Micro-GA embedded PSO feature selection approach to intelligent facial emotion recognition. IEEE transactions on cybernetics, 47, 1496–1509.

  45. Oliveira, L.S., Sabourin, R., Bortolozzi, F., Suen, C.Y. 2003. A methodology for feature selection using multiobjective genetic algorithms for handwritten digit string recognition. International Journal of Pattern Recognition and Artificial Intelligence, 17, 903–929.

  46. Pashaei, E., Aydin, N. 2017. Binary black hole algorithm for feature selection and classification on biological data. Applied Soft Computing Journal, 56, 94–106.

  47. Sayed, G.I., Tharwat, A., Hassanien, A.E. 2019. Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection. Applied Intelligence, 49, 188–205.

  48. Sindhu, R., Ngadiran, R., Yacob, Y.M., Zahri, N.A.H., Hariharan, M. 2017. Sine–cosine algorithm for feature selection with elitism strategy and new updating mechanism. Neural Computing and Applications, 28, 2947–2958.

  49. Soufan, O., Kleftogiannis, D., Kalnis, P., Bajic, V.B. 2015. DWFS: A wrapper feature selection tool based on a parallel Genetic Algorithm. PLoS ONE, 10, 1–23.

  50. Tian, H., Shen, H. 2005. Hamming distance and hop count based classification for multicast network topology inference. In 19th International Conference on Advanced Information Networking and Applications, 267–272.

  51. Wang, L., Ni, H., Yang, R., Pappu, V., Fenn, M.B., Pardalos, P.M. 2014. Feature selection based on meta-heuristics for biomedicine. Optimization Methods and Software, 29, 703–719.

  52. Xue, B., Zhang, M., Browne, W.N. 2013. Particle swarm optimization for feature selection in classification: A multiobjective approach. IEEE Transactions on Cybernetics, 43, 1656–1671.

  53. Xue, B., Zhang, M., Browne, W.N., Yao, X. 2016. A survey on evolutionary computation approaches to feature selection. IEEE Transactions on Evolutionary Computation, 20, 606–626.

  54. Zawbaa, H.M., Emary, E., Grosan, C. 2016. Feature selection via chaotic antlion optimization. PLoS ONE, 11, 1–21.

  55. Zhang, X., Xu, Y., Yu, C., Heidari, A.A., Li, S., Chen, H., Li, C. 2020. Gaussian mutational chaotic fruit fly-built optimization and feature selection. Expert Systems with Applications. doi:10.1016/j.eswa. 2019.112976.

  56. Zheng, L., Diao, R., Shen, Q. 2015. Self-adjusting harmony search-based feature selection. Soft Computing, 19, 1567–1579.


ARTICLE INFORMATION

Received: 2019-04-15
Revised: 2020-07-14
Accepted: 2020-08-04
Available Online: 2020-09-01


Cite this article:

Mandal, A.K., Sen, R., Chakraborty, B. 2020. Analysis of various transfer functions for binary owl search algorithm in feature selection problem. International Journal of Applied Science and Engineering, 17, 281–297. https://doi.org/10.6703/IJASE.202009_17(3).281

  Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Other people also read ...

doaj logo

 

2.3
2022CiteScore
 
 
48th percentile
Powered by  Scopus
 
 

SCImago Journal & Country Rank

IJASE - Most Read Articles

IJASE - Most popular articles