Study of Double SMO Algorithm Based on Attributes Reduction

  • Chen Chen
  • Liu Hong
  • Haigang Song
  • Xueguang Chen
  • TieMin Hou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5552)


To solve the classification problem in data mining, this paper proposes double SMO algorithm based on attributes reduction. Firstly attributes reduction deletes irrelevant attributes (or dimensions) to reduce data amount, consequently the total calculation is reduced, the training speed is fastened and Classification mode is easy to understand. Secondly applying SMO algorithm on the sampling dataset to get the approximate separating hyperplane, and then we obtain all the support vectors of original dataset. Finally again use SMO algorithm on the support vectors to get the final separating hyperplane. It is shown in the experiments that the algorithm reduces the memory space, effectively avoids the noise points’ effect on the final separating hyperplane and the precision of the algorithm is better than Decision Tree, Bayesian and Neural Network.


Data mining Support vector machine Attribute reduction Training algorithm 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Chen Chen
    • 1
  • Liu Hong
    • 1
  • Haigang Song
    • 2
  • Xueguang Chen
    • 1
  • TieMin Hou
    • 3
  1. 1.Institute of System EngineeringHuazhong University of Science and TechnologyWuhanP.R. China
  2. 2.Basic Research Service of the Ministry of Science and Technology of the P. R. ChinaBeijingP.R. China
  3. 3.Key Lab. for Image Processing and Intelligent controlHuazhong University of Science and TechnologyWuhanP.R. China

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