A PSO Based Incremental Hyper-Sphere Partitioning Approach to Classification

Binge Zheng, Sheng-Uei Guan and Jinghao Song
Additional contact information
Binge Zheng: Department of Computer Science and Software Engineering, Liverpool University, Jiangsu, China
Sheng-Uei Guan: Department of Computer Science and Software Engineering, Xian Jiaotong-Liverpool University, Jiangsu, China
Jinghao Song: Department of Computer Science and Software Engineering, Liverpool University, Jiangsu, China

International Journal of Applied Evolutionary Computation (IJAEC), 2014, vol. 5, issue 2, 58-71

Abstract: This paper proposes an incremental hyper-sphere partitioning approach for classification problems. Hyper-spheres that are close to the classification boundaries of a given problem are searched using an incremental approach based upon Particle Swarm Optimization (PSO). This new algorithm is proposed to tackle the difficulty of classification problems caused by the complex pattern relationship with a simplified expert rule structure. We solve classification problems through a combination of hyper-sphere partitioning and a Euclidean-distance based partitioning approach. Moreover, an incremental approach combined with output partitioning and pattern reduction is applied to cope with the curse of dimensionality. The algorithm is tested with seven datasets. The experimental results show that this proposed algorithm outperforms ILEGA (our former research work) and normal GA significantly in the final classification accuracy. In terms of the time complexity, it also gains significant improvement in comparison with ILEGA.

Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/ijaec.2014040104 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jaec00:v:5:y:2014:i:2:p:58-71

Access Statistics for this article

International Journal of Applied Evolutionary Computation (IJAEC) is currently edited by Sukhpal Singh Gill

More articles in International Journal of Applied Evolutionary Computation (IJAEC) from IGI Global
Bibliographic data for series maintained by Journal Editor ().