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Incremental Hyper-Sphere Partitioning for Classification

Author

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  • Jinghao Song

    (Department of Computer Science and Software Engineering, Liverpool University, Jiangsu, China)

  • Sheng-Uei Guan

    (Department of Computer Science and Software Engineering, Xi'an Jiaotong-Liverpool University, Jiangsu, China)

  • Binge Zheng

    (Department of Computer Science and Software Engineering, Liverpool University, Jiangsu, China)

Abstract

In this paper, an Incremental Hyper-Sphere Partitioning (IHSP) approach to classification on the basis of Incremental Linear Encoding Genetic Algorithm (ILEGA) is proposed. Hyper-spheres approximating boundaries to a given classification problem, are searched with an incremental approach based on a unique combination of genetic algorithm (GA), output partitioning and pattern reduction. ILEGA is used to cope with the difficulty of classification problems caused by the complex pattern relationship and curse of dimensionality. Classification problems are solved by a simple and flexible chromosome encoding scheme which is different from that was proposed in Incremental Hyper-plane Partitioning (IHPP) for classification. The algorithm is tested with 7 datasets. The experimental results show that IHSP performs better compared with those classified using hyper-planes and normal GA.

Suggested Citation

  • Jinghao Song & Sheng-Uei Guan & Binge Zheng, 2014. "Incremental Hyper-Sphere Partitioning for Classification," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 5(2), pages 72-88, April.
    • Handle: RePEc:igg:jaec00:v:5:y:2014:i:2:p:72-88
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