FEATURE SELECTION USING BIO INSPIRED ALGORITHMS
DOI:
https://doi.org/10.61841/52tmed74Keywords:
Crow Search Algorithm;, Feature Selection, Fish Swarm Algorithm;, Optimization, Swarm Intelligence.Abstract
-- Bio-inspired computing optimization algorithms are recently developed algorithms which are influenced by the biological progression of nature. These algorithms are proving to be better than the traditional machine learning algorithms as they can determine optimal solution of complex problems in the field of science. This paper presents the 10 recent bio-inspired algorithms as well as their diverse applications, especially in the medical field namely- Artificial Bee Colony (ABC) Algorithm, Fish Swarm Algorithm (FSA), Cat Swarm Optimization (CSO), Whale Optimization Algorithm (WOA), Artificial Algae Algorithm (AAA), Cuttlefish Algorithm (CFA), Bat Algorithm(BA), Grasshopper Optimization Algorithm(GOA), Ant Lion Optimization Algorithm(ALO) and Crow Search Algorithm (CSA). We are trying to analyze the ways through which they mimic evolutionary operators. Also, we study and analyse the results with the help of feature selection using bio-inspired algorithms. It is an essential process that is relevant for predictive analysis. It is considered to be the most important step before using machine learning algorithms. Crow Search algorithm is used for feature selection and gives an accuracy of 98% by using Support vector machine(SVM).
Downloads
References
1. Madhu Sadhvi (Sep 26, 2017), Chapter 1: Complete Linear Regression with Math, Medium. www.medium.com/deep-math-machine-learning-ai/
2. Reena Shaw (June 26, 2019), The 10 Best Machine Learning Algorithms for Data Science Beginners,Dataquest.https://www.dataquest.io/blog/top-10-machine-learning-algorithms-for-beginners/
3. Adel Sabry Eesa, Adnan Mohsin Abdulazeez Brifcani, Zeynep Orman , “Cuttlefish Algorithm – A Novel Bio-Inspired Optimization Algorithm”,International Journal of Scientific & Engineering Research, Volume 4, Issue 9, September 20
4. Problems Cuttlefish Algorithm”, International Journal of Computer and Information Engineering Vol:8, No:9, 2014
5. Saurav Kaushik (December 1, 2016), Introduction to Feature Selection methods with an example, Analytics vidhya. https://www.analyticsvidhya.com/blog/2016/12/introduction-to-feature-selection-methods-with-an- example-or-how-to-select-the-right-variables/
6. M.P. Saka, Ibrahim Aydogdu “Analysis of Swarm Intelligence–Based Algorithms for Constrained Optimization”, Swarm Intelligence and Bio-Inspired Computation, 2013
7. Xin-She Yang, Mehmet Karamanoglu,‘Swarm Intelligence and Bio-Inspired Computation’, Swarm Intelligence and Bio-Inspired Computation, 2013.
8. Amir H. Gandomi et al, ‘ Metaheuristic Algorithms in Modeling and Optimization’, Metaheuristic Applications in Structures and Infrastructures, pp.1-24.
9. Shu-Chuan Chu et al, ‘Cat swarm optimization”, Conference: PRICAI 2006: Trends in Artificial Intelligence, August 7-11, 2006.
10. Fran Sérgio Lobato & Valder Steffen Jr, “Fish swarm optimization algorithm applied to engineering system design”, Latin American Journal of Solids and Structures, vol.11 no.1 Jan.2014.
11. Mohit Kumar et al, “Optimization of Some Standard Functions using Artificial Algae Algorithm”, International Journal of Engineering Research & Technology (IJERT), ACMEE - 2016 Conference Proceedings.
12. Md.Al-imran Roton (May 26, 2016), Bat algorithm and applications, Slideshare. https://www.slideshare.net/roton007/bat-algorithm-and-application
13. Bhavana Bansal & Anita Sahoo, “Bat Algorithm for Full Model Selection in Classification: A Multi-objective Approach”, IJIACS,Volume 4,March 2015 .
14. Primitivo Díaz et al, “An Improved Crow Search Algorithm Applied to Energy Problems”, March 2018
15. “Artificial bee colony algorithm”, Wikimedia Foundation, 12 Jan 2020, https://en.wikipedia.org/wiki/Artificial_bee_colony_algorithm
16. Kamalpreet Kaur Dhaliwal et al, “Modified Cat Swarm Optimization Algorithm for Design and Optimization of IIR BS Filter”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (3), 2015
17. A.Y. Abdelaziz, A. Fathy, “A novel approach based on crow search algorithm for optimal selection of conductor size in radial distribution networks”, Engineering Science and Technology, Journal 20 (2017) 391402
18. Askarzadeh, A., “A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm”, Computers & Structures, Volume 169, June 2016, Pages 1-12.
19. Hala M.Alshamlan, Ghada H.Badr, Yousef A.Alohali, “Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification”, Computational Biology and Chemistry, Volume 56, June 2015, Pages 49-60
20. N. Vinayak (Jun 2010, 2015), ABC Algorithm - Basic Algorithm on Optimization, Slideshare. https://www.slideshare.net/VinayakNayak5/abc-algorithm-49203658
21. Shahrzad Saremi, Seyedali Mirjalili, Andrew Lewis, “Grasshopper Optimisation Algorithm: Theory and application”, Advances in Engineering Software 105:30-47 · March 2017.
22. Neve et al., “Application of Grasshopper Optimization Algorithm for Constrained and Unconstrained Test Functions”, International Journal of Swarm Intelligence and Evolutionary Computation, 2017.
23. Xin-She Yang,“Nature-Inspired Algorithms and Applied Optimization Studies”, Computational Intelligence, 2018.
24. A. A. Abou El Ela ; Ragab A. El-Sehiemy ; A. M. Shaheen ; A. S. Shalaby, “Application of the crow search algorithm for economic environmental dispatch“, 2017 Nineteenth International Middle East Power Systems Conference (MEPCON)
25. Ahmed Fouad Ali (Apr 20, 2018), Grasshopper optimization algorithm, slideshare. https://www.slideshare.net/afar1111/grasshopper-optimization-algorithm
26. Ahmed Fouad Ali (May 13, 2019), Crow Search Algorithm, Slideshare. https://www.slideshare.net/afar1111/crow-search-algorithm?qid=80054cf7-2297-400d-a60a- 6ff013736fea&v=&b=&from_search=1.
27. Almoataz Y. Abdelaziz, Ahmed Fathy, “A novel approach based on crow search algorithm for optimal selection of conductor size in radial distribution networks”,Engineering Science and Technology, an International Journal, Volume 20, Issue 2, April 2017, Pages 391-402.
28. Budi Santosa ; Mirsa Kencana Ningrum, “Cat Swarm Optimization for Clustering”, IEEE, 2009 International Conference of Soft Computing and Pattern Recognition.
29. Pawn Bhambu, “Artificial Bee Colony Algorithm: A Survey”, International Journal of Computer Applications 149(4):11-19 · September 2016.
30. Ryo Takano, Tomohiro Harada, Hiroyuki Sato, Keiki Takadama, “Artificial Bee Colony Algorithm Based on Local Information Sharing in Dynamic Environment”, Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Volume 1 pp 627-641.
31. Yunfeng Xu, Ping Fan and Ling Yuan, “A Simple and Efficient Artificial Bee Colony Algorithm”,Mathematical Problems in Engineering Volume 2013.
32. Lijun Sun, Tianfei Chen and Qiuwen Zhang, “An Artificial Bee Colony Algorithm with Random Location Updating”, Scientific Programming, Volume 2018, Article ID 2767546, 9 pages.
33. Fran Sérgio Lobato, Valder Steffen, Jr, “Fish Swarm Optimization Algorithm Applied to Engineering System Design”, Latin American Journal of Solids and Structures 11(1):143-156 · January 2014.
34. Ashraf Darwish, “Bio-inspired computing: Algorithms review, deep analysis, and the scope of applications”, Future Computing and Informatics Journal 3, 2018.
35. Chulmin Yun et al, “Feature Subset Selection Based on Bio-Inspired Algorithms”, Journal of Information Science And Engineering, 1667-1686 (2011).
36. E.Emarya, Hossam M.Zawbaa, Aboul Ella Hassanien, “Binary grey wolf optimization approaches for feature selection “, Neurocomputing, Volume 172, 8 January 2016, Pages 371-381.
37. Shilpa Kukreja, Surjeet Dalal, “Nature-Inspired Algorithms: Critical Study”, International Journal of Recent Research Aspects, 2016, pp. 38-40.
38. V.R. Balasaraswathi et al, “ Chaotic CuttleFish Algorithm for Feature Selection of Intrusion Detection System”, International Journal of Pure and Applied Mathematics Volume 119 No. 10 2018, 921-935.
39. Ms. Neeta Nemade , Mr. Dhiraj Rane, “A Review on Bio-Inspired Computing Algorithms and Application”, IOSR Journal of Computer Engineering (IOSR-JCE), PP 12-19.
40. Eslam Hamouda et al, “Ant Lion Optimization algorithm for kidney exchanges”, PLoS One, May 2018.
41. Mirjalili S. The Ant Lion Optimizer. Advances in Engineering Software. 2015;83(2015):80–98.
42. Shallu Sehgal, Dr Manisha Agarwal, Dr Deepak Gupta, Arun Bashambu “Comparative Study on Machine Learning and Data Mining Techniques for Diseases Diagnosis”, International Journal of Management, IT and Engineering Vol. 9 Issue 1(1), January 2019, ISSN: 2249-0558.
43. Shallu Sehgal, Manisha Agarwal, “Analogous Examination of Various Machine Learning Algorithm Applied to Big Data”, IEEE International Conference on Advances in Computing, Communication Control and Networking (ICACCC-2018 Co -Sponsored by IEEE UP Section), October 2018.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
