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Classification of Brain Glioma by Using SVMs Bagging with Feature Selection

  • Guo-Zheng Li
  • Tian-Yu Liu
  • Victor S. Cheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3916)

Abstract

The degree of malignancy in brain glioma needs to be assessed by MRI findings and clinical data before operations. There have been previous attempts to solve this problem by using fuzzy max-min neural networks and support vector machines (SVMs), while in this paper, a novel algorithm named PRIFEB is proposed by combining bagging of SVMs with embedded feature selection for its individuals. PRIFEB is compared with the general case of bagging on UCI data sets, experimental results show PRIFEB can obtain better performance than the general case of bagging. Then, PRIFEB is used to predict the degree of malignancy in brain glioma, computation results show that PRIFEB obtains better accuracy than other several methods like bagging of SVMs and single SVMs does.

Keywords

Support Vector Machine Feature Selection Prediction Accuracy Brain Glioma Embed Feature Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Guo-Zheng Li
    • 1
    • 2
  • Tian-Yu Liu
    • 2
  • Victor S. Cheng
    • 3
  1. 1.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina
  2. 2.School of Computer Engineering and ScienceShanghai UniversityShanghaiChina
  3. 3.Institute of Biomedical InstrumentShanghai Jiao Tong UniversityShanghaiChina

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