A New Binary Particle Swarm Optimisation Algorithm for Feature Selection
Feature selection aims to select a small number of features from a large feature set to achieve similar or better classification performance than using all features. This paper develops a new binary particle swarm optimisation (PSO) algorithm (named PBPSO) based on which a new feature selection approach (PBPSOfs) is developed to reduce the number of features and increase the classification accuracy. The performance of PBPSOfs is compared with a standard binary PSO based feature selection algorithm (BPSOfs) and two traditional feature selection algorithms on 14 benchmark problems of varying difficulty. The results show that PBPSOfs can be successfully used for feature selection to select a small number of features and improve the classification performance over using all features. PBPSOfs further reduces the number of features selected by BPSOfs and simultaneously increases the classification accuracy, especially on datasets with a large number of features. Meanwhile, PBPSOfs achieves better performance than the two traditional feature selection algorithms. In addition, the results also show that PBPSO as a general binary optimisation technique can achieve better performance than standard binary PSO and uses less computational time.
KeywordsBinary particle swarm optimisation Feature selection Classification
Unable to display preview. Download preview PDF.
- 1.Caruana, R., Freitag, D.: Greedy attribute selection. In: International Conference on Machine Learning (ICML 1994), pp. 28–36. Morgan Kaufmann (1994)Google Scholar
- 4.Engelbrecht, A.P.: Computational intelligence: an introduction, 2nd edn. Wiley (2007)Google Scholar
- 5.Frank, A., Asuncion, A.: UCI machine learning repository (2010)Google Scholar
- 6.Gutlein, M., Frank, E., Hall, M., Karwath, A.: Large-scale attribute selection using wrappers. In: IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2009), pp. 332–339 (2009)Google Scholar
- 8.Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)Google Scholar
- 9.Kennedy, J., Eberhart, R.: A discrete binary version of the particle swarm algorithm. In: IEEE International Conference on Systems, Man, and Cybernetics (1997), Computational Cybernetics and Simulation, vol. 5, pp. 4104–4108 (1997)Google Scholar
- 10.Khanesar, M., Teshnehlab, M., Shoorehdeli, M.: A novel binary particle swarm optimization. In: Mediterranean Conference on Control Automation (MED 2007), pp. 1–6 (2007)Google Scholar
- 13.Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: IEEE International Conference on Evolutionary Computation (CEC 1998), pp. 69–73 (1998)Google Scholar
- 14.Sudholt, D., Witt, C.: Runtime analysis of binary PSO. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation (GECCO 2008), pp. 135–142. ACM, New York (2008)Google Scholar
- 15.Whitney, A.: A direct method of nonparametric measurement selection. IEEE Transactions on Computers C-20(9), 1100–1103 (1971)Google Scholar
- 16.Xue, B., Cervante, L., Shang, L., Browne, W.N., Zhang, M.: A multi-objective particle swarm optimisation for filter based feature selection in classification problems. Connection Science (2012)Google Scholar
- 17.Xue, B., Zhang, M., Browne, W.N.: New fitness functions in binary particle swarm optimisation for feature selection. In: IEEE Congress on Evolutionary Computation (CEC 2012), pp. 2145–2152 (2012)Google Scholar