Abstract
In order to solve the problem that the evolutionary algorithm based class center classification algorithm easily falls into a local optimum later in the process, this paper proposes a Gene Expression Programming(GEP) classification algorithm which is optimized by Particle Swarm Optimization(PSO). It’s named after the GEPSO-Classification Algorithm, and the word GEPSO comes from the combination of the word GEP and PSO. This algorithm first finds a suboptimal solution on the merit that GEP can converge rapidly in the early stage, then with this suboptimal solution, the algorithm searches the optimal solution on the merit that PSO is more likely to converge to the optimal solution. The experimental result shows that this algorithm has a better performance on classification.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Jiawei, H., Micheline, K.: Data Mining: Concept and Techniques. China Machine Press, Beijing (2011)
Guojun, M., Lijuan, D., Wang, S.: The Principle and Algorithm of Data Ming. TsingHua University Press, Beijing (2007)
Andries, P.E.: Computer Intelligence An Introduction. TsingHua University Press, Beijing (2010)
Ferreira, C.: Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence. SCI, vol. 21. Springer, Heidelberg (2006)
Zengwei, Z., Ping, W.: The Study of Naive Bayes algorithm based Genetic Algorithm Classification. Computer Engineering and Design (2012)
Chi, Z., Weimin, X., Tirpak: Evolving Accurate and Compact Classification Rules with Gene Expression Programming. IEEE Transactions on Evolutionary Computation (2003)
Weihong, W., Wei, R., Qu, L.: Decision Tree Algorithm by Gene Expression Programming Based on Differential Evolution. Computer Engineering (2011)
Rui, D., Hongbing, D., Xianbin, F.: Particle Swarm Optimization Genetic Algorithm Applied in Classification Question. Computer Engineering (2009)
Licheng, J., Jing, L., Weicai, Z.: Co-evolutionaryComputation and Multi-agent System. Science Press, Beijing (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, W., Jin, D., Li, Q., Fang, Z., Yang, J. (2013). The GEPSO-Classification Algorithm. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds) Advanced Data Mining and Applications. ADMA 2013. Lecture Notes in Computer Science(), vol 8346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53914-5_49
Download citation
DOI: https://doi.org/10.1007/978-3-642-53914-5_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-53913-8
Online ISBN: 978-3-642-53914-5
eBook Packages: Computer ScienceComputer Science (R0)