Advanced Search
Volume 31 Issue 3
Dec.  2010
Turn off MathJax
Article Contents
Wang Ting-hua, Tian Sheng-feng, Huang Hou-kuan. Feature Weighted Support Vector Machine[J]. Journal of Electronics & Information Technology, 2009, 31(3): 514-518. doi: 10.3724/SP.J.1146.2007.01711
Citation: Wang Ting-hua, Tian Sheng-feng, Huang Hou-kuan. Feature Weighted Support Vector Machine[J]. Journal of Electronics & Information Technology, 2009, 31(3): 514-518. doi: 10.3724/SP.J.1146.2007.01711

Feature Weighted Support Vector Machine

doi: 10.3724/SP.J.1146.2007.01711
  • Received Date: 2007-10-31
  • Rev Recd Date: 2008-04-07
  • Publish Date: 2009-03-19
  • Support vector machine has been applied in many research fields, such as pattern recognition and function estimate. There is a shortcoming in Weighted SVM and Fuzzy SVM, which take the importance of sample into account but neglect the relative importance of each feature with respect to the classification task. In this paper a SVM approach is proposed based on the feature weighting, i.e. Feature Weighted SVM (FWSVM). This method first estimates the relative importance (weight) of each feature by computing the information gain. Then, it utilizes the weights for computing the inner product and Euclidean distance in kernel functions. In this way the computing of kernel function can avoid being dominated by trivial relevant or irrelevant features. Theoretical analysis and experimental results show that the FWSVM is more robust and has the better performance of generalization than the traditional SVM.
  • loading
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (4553) PDF downloads(3211) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return