CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Particle Swarm Optimization based Feature Selection

by Neha, Jyoti Vashishtha
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 6
Year of Publication: 2016
Authors: Neha, Jyoti Vashishtha
10.5120/ijca2016910789

Neha, Jyoti Vashishtha . Particle Swarm Optimization based Feature Selection. International Journal of Computer Applications. 146, 6 ( Jul 2016), 11-17. DOI=10.5120/ijca2016910789

@article{ 10.5120/ijca2016910789,
author = { Neha, Jyoti Vashishtha },
title = { Particle Swarm Optimization based Feature Selection },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 146 },
number = { 6 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 11-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume146/number6/25401-2016910789/ },
doi = { 10.5120/ijca2016910789 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:49:37.769241+05:30
%A Neha
%A Jyoti Vashishtha
%T Particle Swarm Optimization based Feature Selection
%J International Journal of Computer Applications
%@ 0975-8887
%V 146
%N 6
%P 11-17
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Feature Selection is a pre-processing step in knowledge discovery from data (KDD) which aims at retrieving relevant data from the database beforehand. It imparts quality to the results of data mining tasks by selecting optimal feature set from larger set of features. Various feature selection techniques have been proposed in past which, unfortunately, suffer from unavoidable problems such as high computational cost and getting stuck into the local optima. Evolutionary algorithms such as Particle Swarm Optimization (PSO) possess immense abilities to explore a large search space and rarely fall into local optima thus making them a nice choice for feature selection. In this paper, we have explored pros and cons of traditional and PSO based feature selection techniques and suggested some effective changes in existing approaches.

References
  1. Lin, S. W., Ying, K. C., Chen, S. C. and Lee, Z. J. 2008.Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Systems with Applications, Vol. 35, No. 4, pp. 1817-1824.
  2. Vashishtha, J., Kumar, D., Ratnoo, S. and Kapila. 2011. Mining comprehensible and interesting rules: a genetic algorithm approach. International Journal of Computer Applications, Vol. 31, No. 1, pp.39–47.
  3. Yusta, S. C. 2009. Different meta-heuristic strategies to solve the feature selection problem. Pattern Recognition Letters. Vol. 30, No. 5, pp. 525–534.
  4. Han, J., Kamber, M. and Pei, J. 2011. Data Mining: Concepts and Techniques, Third Edition, Morgan Kaufmann.
  5. Tran, B., Xue, B. and Zhang, M. 2014. Overview of Particle Swarm Optimization for Feature Selection in Classification. In Proceeding of 10th International Conference on Simulated Evolution and Learning, Springer, Dunedin, New Zealand, pp. 605–617.
  6. Visalakshi, S. and Radha, V. 2015. Wrapper based Feature Selection and Classification for Real Time Dataset. International Journal of Emerging Technologies in Computational and Applied Sciences, Vol. 3, pp. 306–311.
  7. Lane, M. C., Xue, B., Liu, I. and Zhang, M. 2013. Particle Swarm Optimization and Statistical Clustering for Feature Selection. Advances in Artificial Intelligence, Lecture Notes in Computer ScienceHeidelberg, Springer, Vol. 8272, pp. 214–220.
  8. Lane, M. C., Xue, B., Liu, I. and Zhang, M. 2014. Gaussian Based Particle Swarm Optimization and Statistical Clustering for Feature Selection. Evolutionary Computation in Combinatorial Optimization, Lecture Notes in Computer Science, Heidelberg, Springer, Vol. 8600, pp. 133–14.
  9. Chandrashekar, G. and Sahin, F. 2014. A survey on feature selection methods. Computers & Electrical Engineering, Elsevier. Vol. 40, No. 1, pp. 16–28.
  10. Guan, J., Han, F. and Yang, S. 2013. A new gene selection method for microarray data based on PSO and informativeness metric. Intelligent Computing Theories and Technology, Lecture Notes in Computer Science, Heidelberg, Springer, Vol. 7996, pp. 145-154.
  11. Pathak, A. and Vashishtha, J. 2015. Classification Rule and Exception Mining Using Nature Inspired Algorithms. International Journal of Computer Science and Information Technologies. Vol. 6, No. 3, pp. 3023-3030.
  12. Yeoman, T. B., Xue, B. and Zhang, M. 2015. Particle Swarm Optimization for Feature Selection: A Size-Controlled Approach. In Proceedings of the 13th Australasian Data Mining Conferences on Research and Practice in Information Technology, Sydney, Australia Vol. 168.
  13. Azevedo, G. L., Cavalcanti, G.D. and E CB Filho, C. 2007. An approach to feature selection for keystroke dynamics systems based on PSO and feature weighting. In Proceedings of 2007 Congress on Evolutionary Computation, Singapore, IEEE, pp. 3577–3584.
  14. Xue, B., Zhang, M. and Browne, W. N. 2012. Multi-objective Particle Swarm Optimization for Feature Selection. In Proceedings of 14th Annual Conference on Genetic and Evolutionary Computation, ACM, Philadelphia, Pennsylvania, USA, pp. 81–88.
  15. Mohemmed, A. W., Zhang, M. and Johnston, M. 2009. Particle Swarm Optimization based Adaboost for face detection. In Proceedings of 2009 Congress on Evolutionary Computation, Trondheim, IEEE, pp. 2494–2501.
  16. Xue, B., Zhang, M. and Browne, W. N. 2012. New fitness functions in binary particle swarm optimization for feature selection. In Proceedings of 2012 Congress on Evolutionary Computation, Brisbane, QLD, IEEE, pp. 1–8.
  17. Xue, B., Qin, A.K. and Zhang, M. 2014. An archive based particle swarm optimization for feature selection in classification. In Proceedings of 2014 Congress on Evolutionary Computation, Beijing, IEEE, pp. 3119–3126.
  18. Kennedy, J. and Eberhart, R. 1995. Particle swarm optimization. In Proceeding of International Conference on Neural Networks. IEEE, Vol. 4, pp. 1942–1948.
  19. Eberhart, R. and Shi Y. 2007. Computational Intelligence, 1st Edition, Morgan Kaufmann..
  20. Shi, Y. 2004. Particle Swarm Optimization. In Proceeding of International Conference on Neural Networks Society IV, IEEE, No. 1, pp. 8-13.
  21. Shi, Y. and Eberhart, R. 1998. A Modified Particle Swarm Optimizer. In Proceeding of International Conference on Evolutionary Computation, Anchorage, AK, IEEE, pp. 69–73.
  22. Xue, B., Nguyen, S. and Zhang, M. 2014. A New Binary Particle Swarm Optimization Algorithm for Feature Selection. In Proceedings of 17th European Conference on Applications of Evolutionary Computation, Granada, Spain, Springer, pp. 501–513.
  23. Zhang, Y., Gong, D., Hu, Y. and Zhang, W. 2015. Feature selection algorithm based on bare bones particle swarm optimization. Neurocomputing, Elsevier, Vol. 148, pp. 150–157.
  24. Sousa, T., Silva, A. and Neves A. 2004. Particle Swarm based Data Mining Algorithms for classification tasks. Parallel Computing, Elsevier, Vol. 30, No. 5–6, pp. 767–783.
  25. Xue, B., Zhang, M. and Browne, W.N. 2014. Particle swarm optimization for feature selection in classification: Novel initialization and updating mechanisms. Applied Soft Computing, Elsevier, Vol. 18, pp. 261–276.
  26. Dash, M. and Liu, H. 1997. Feature selection for classification. Intelligent data analysis, Elsevier, Vol. 1, No. 3, pp. 131–156.
  27. Unler, A. and Murat, A. 2010. A discrete particle swarm optimization method for feature selection in binary classification problems. European Journal of Operational Research, Elsevier, Vol. 206, No. 3, pp. 528–539.
  28. Wang, X., Yang, J., Teng, X., Xia, W. and Jensen, R. 2007. Feature selection based on rough sets and particle swarm optimization. Pattern Recognition Letters, Elsevier, Vol. 28, No. 4, pp. 459–471.
  29. Cervante, L., Xue, B., Zhang, M. and Shang, L. 2012. A Dimension Reduction Approach to Classification Based on Particle Swarm Optimization and Rough Set Theory. In Proceedings of 25thAustralasian Joint Conference on Artificial Intelligence, Sydney, Springer, pp. 313-325.
  30. Cervante, L., Xue, B., Zhang, M. and Shang, L. 2012. Binary particle swarm optimization for feature selection: A filter based approach. In Proceedings of 2012 Congress on Evolutionary Computation, Brisbane, QLD, IEEE, pp. 1–8.
  31. Chuang, L.Y., Chang, H.W., Tu, C.J. and Yang, C.H. 2008. Improved Binary PSO for Feature Selection Using Gene Expression Data. Computational Biology and Chemistry, Elsevier, Vol. 32, No. 1, pp. 29–38.
  32. Yang, C.S., Chuang, L.Y., Ke, C.H. and Yang, C.H. 2008. Boolean binary particle swarm optimization for feature selection. In Proceedings of 2008 Congress on Evolutionary Computation, Hong Kong, IEEE, pp. 2093–2098.
  33. Yang, C.S., Chuang, L.Y., Li J.C., and Yang, C. H. 2008. Chaotic maps in binary particle swarm optimization for feature selection. In Proceedings of Conference on Soft Computing in Industrial Applications, Muroran, IEEE, pp. 107–112.
  34. Chuang, L.Y., Tsai, S.W. and Yang, C.H. 2011. Improved binary particle swarm optimization using catfish effect for feature selection. Expert Systems with Applications, Elsevier, Vol. 38, No. 10, pp. 12699–12707.
  35. Wang, J., Zhao, Y. and Liu, P. 2010. Effective feature selection with Particle Swarm Optimization based one-dimension searching. In 3rd International Symposium on Systems and Control in Aeronautics and Astronautics, IEEE, pp. 702– 705.
  36. Liu, Y., Wang, G., Chen, H., Dong, H., Zhu, X. and Wang, S. 2011. An Improved Particle Swarm Optimization for Feature Selection. Journal of Bionic Engineering, Science Direct, Vol. 8, No. 2, pp. 191–200.
  37. Sahu, B. and Mishra, D. 2012. A Novel Feature Selection Algorithm using Particle Swarm Optimization For Cancer Microarray Data. Procedia Engineering, Elsevier, Vol. 38, pp. 27–31.
  38. Xue, B., Zhang, M. and Browne, W.N. 2013. Particle swarm optimization for feature selection in classification: A multi-objective approach. Cybernetics, IEEE, Vol. 43, No. 6, pp. 1656-1671.
  39. Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. Evolutionary Computation, IEEE, Vol. 6, No. 2, pp. 182–197.
  40. Sierra, M. R. and Coello, C. A. C. 2005. Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and ∈-Dominance. Evolutionary Multi-Criterion Optimization. Lecture notes in computer science, Guanajuato, Mexico, Springer, pp. 505–519.
Index Terms

Computer Science
Information Sciences

Keywords

Particle Swarm Optimization (PSO) Evolutionary Algorithm (EA) Feature Selection (FS).

Powered by PhDFocusTM