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Imbalanced Data Set CSVM Classification Method Based on Cluster Boundary Sampling

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  • Peng Li
  • Tian-ge Liang
  • Kai-hui Zhang

Abstract

This paper creatively proposes a cluster boundary sampling method based on density clustering to solve the problem of resampling in IDS classification and verify its effectiveness experimentally. We use the clustering density threshold and the boundary density threshold to determine the cluster boundaries, in order to guide the process of resampling more scientifically and accurately. Then, we adopt the penalty factor to regulate the data imbalance effect on SVM classification algorithm. The achievements and scientific significance of this paper do not propose the best classifier or solution of imbalanced data set and just verify the validity and stability of proposed IDS resampling method. Experiments show that our method acquires obvious promotion effect in various imbalanced data sets.

Suggested Citation

  • Peng Li & Tian-ge Liang & Kai-hui Zhang, 2016. "Imbalanced Data Set CSVM Classification Method Based on Cluster Boundary Sampling," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-9, July.
    • Handle: RePEc:hin:jnlmpe:1540628
    DOI: 10.1155/2016/1540628
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    Cited by:

    1. Julio Cesar Munguía Mondragón & Eréndira Rendón Lara & Roberto Alejo Eleuterio & Everardo Efrén Granda Gutirrez & Federico Del Razo López, 2023. "Density-Based Clustering to Deal with Highly Imbalanced Data in Multi-Class Problems," Mathematics, MDPI, vol. 11(18), pages 1-15, September.

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