An ensemble SVM using entropy-based attribute selection

  title={An ensemble SVM using entropy-based attribute selection},
  author={Ruhai Lei and Xiaoxiao Kong and Xuesong Wang},
  journal={2010 Chinese Control and Decision Conference},
In order to improve the generalization performance of support vector machine (SVM), a kind of ensemble SVM using an entropy-based attribute selection method was proposed. An entropy metric based on similarity between objects was designed to evaluate the importance degree of each attribute and so as to obtain a set of important attributes. Based on the set of important attributes, the Bagging method was used to generate sub-SVMs and then the majority voting rule was adopted to obtain the final… CONTINUE READING
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