Using SOM-Based Data Binning to Support Supervised Variable Selection

  • Sampsa Laine
  • Timo Similä
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3316)


We propose a robust and understandable algorithm for supervised variable selection. The user defines a problem by manually selecting the variables Y that are used to train a Self-Organizing Map (SOM), which best describes the problem of interest. This is an illustrative problem definition even in multivariate case. The user also defines another set X, which contains variables that may be related to the problem. Our algorithm browses subsets of X and returns the one, which contains most information of the user’s problem. We measure information by mapping small areas of the studied subset to the SOM lattice. We return the variable set providing, on average, the most compact mapping. By analysis of public domain data sets and by comparison against other variable selection methods, we illustrate the main benefit of our method: understandability to the common user.


Variable Selection Variable Selection Method Horse Power Well Match Unit Variable Selection Algorithm 
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 2004

Authors and Affiliations

  • Sampsa Laine
    • 1
  • Timo Similä
    • 1
  1. 1.Laboratory of Computer and Information ScienceHelsinki University of TechnologyFinland

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