Abstract
Feature construction can improve the classification performance by constructing high-level features using the original low-level features and function operators. Particle swarm optimisation (PSO) is an powerful global search technique, but it cannot be directly used for feature construction because of its representation scheme. This paper proposes two new representations, pair representation and array representation, which allow PSO to direct evolve function operators. Two PSO based feature construction algorithms (PSOFCPair and PSOFCArray) are then developed. The two new algorithms are examined and compared with the first PSO based feature construction algorithm (PSOFC), which employs an inner loop to select function operators. Experimental results show that both PSOFCPair and PSOFCArray can increase the classification performance by constructing a new high-level feature. PSOFCArray outperforms PSOFCPair and achieves similar results to PSOFC, but uses significantly shorter computational time. This paper represents the first work on using PSO to directly evolve function operators for feature construction.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Azevedo, G., Cavalcanti, G., Filho, E.: An approach to feature selection for keystroke dynamics systems based on PSO and feature weighting. In: IEEE Congress on Evolutionary Computation (CEC 2007), pp. 3577–3584 (2007)
Clerc, M., Kennedy, J.: The particle swarm- explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002)
Engelbrecht, A.P.: Computational intelligence: an introduction, 2. ed. Wiley (2007)
Frank, A., Asuncion, A.: UCI machine learning repository (2010)
Kennedy, J., Eberhart, R.: Particle swarm optimization. IEEE International Conference on Neural Networks. 4, 1942–1948 (1995)
Krawiec, K.: Genetic programming-based construction of features for machine learning and knowledge discovery tasks. Genetic Programming and Evolvable Machines 3(4), 329–343 (2002)
Liu, H., Motada, H. (eds.): Feature extraction, construction and selection: A data mining perspective. Kluwer Academic Publishers, Norwell (1998)
Marinakis, Y., Marinaki, M., Dounias, G.: Particle swarm optimization for pap-smear diagnosis. Expert Systems with Applications 35(4), 1645–1656 (2008)
Muharram, M.A., Smith, G.D.: Evolutionary Feature Construction Using Information Gain and Gini Index. In: Keijzer, M., O’Reilly, U.-M., Lucas, S., Costa, E., Soule, T. (eds.) EuroGP 2004. LNCS, vol. 3003, pp. 379–388. Springer, Heidelberg (2004)
Neshatian, K., Zhang, M., Andreae, P.: A filter approach to multiple feature construction for symbolic learning classifiers using genetic programming. IEEE Transactions on Evolutionary Computation 16(5), 645–661 (2012)
Neshatian, K.: Feature Manipulation with Genetic Programming. PhD thesis, Victoria University of Wellington, Wellington, New Zealand (2010)
Neshatian, K., Zhang, M., Johnston, M.: Feature Construction and Dimension Reduction Using Genetic Programming. In: Orgun, M.A., Thornton, J. (eds.) AI 2007. LNCS (LNAI), vol. 4830, pp. 160–170. Springer, Heidelberg (2007)
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: IEEE International Conference on Evolutionary Computation (CEC 1998), pp. 69–73 (1998)
Unler, A., Murat, A.: A discrete particle swarm optimization method for feature selection in binary classification problems. European Journal of Operational Research 206(3), 528–539 (2010)
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)
Xue, B., Zhang, M., Browne, W.: Particle swarm optimization for feature selection in classification: A multi-objective approach. IEEE Transactions on Cybernetics 43(6), 1656–1671 (2013)
Xue, B., Zhang, M., Dai, Y., Browne, W.N.: PSO for feature construction and binary classification. In: Proceeding of the Fifteenth Annual Conference on Genetic and Evolutionary Computation Conference, GECCO 2013, 137–144 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dai, Y., Xue, B., Zhang, M. (2014). New Representations in PSO for Feature Construction in Classification. In: Esparcia-Alcázar, A., Mora, A. (eds) Applications of Evolutionary Computation. EvoApplications 2014. Lecture Notes in Computer Science(), vol 8602. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45523-4_39
Download citation
DOI: https://doi.org/10.1007/978-3-662-45523-4_39
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45522-7
Online ISBN: 978-3-662-45523-4
eBook Packages: Computer ScienceComputer Science (R0)