Forward Feature Selection Based on Approximate Markov Blanket

  • Min Han
  • Xiaoxin Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7368)


Feature selection has many applications in solving the problems of multivariate time series . A novel forward feature selection method is proposed based on approximate Markov blanket. The relevant features are selected according to the mutual information between the features and the output. To identify the redundant features, a heuristic method is proposed to approximate Markov blanket. A redundant feature is identified according to whether there is a Markov blanket for it in the selected feature subset or not.The simulations based on the Friedman data, the Lorenz time series and the Gas Furnace time series show the validity of our proposed feature selection method.


Feature Selection Redundancy Analysis Markov Blanket Mutual Information 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Min Han
    • 1
  • Xiaoxin Liu
    • 1
  1. 1.Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianChina

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