Maps Ensemble for Semi-Supervised Learning of Large High Dimensional Datasets

  • Elie Prudhomme
  • Stéphane Lallich
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4994)


In many practical cases, only few labels are available on the data. Algorithms must then take advantage of the unlabeled data to ensure an efficient learning. This type of learning is called semi-supervised learning (SSL). In this article, we propose a methodology adapted to both the representation and the prediction of large datasets in that situation. For that purpose, groups of non-correlated attributes are created in order to overcome problems related to high dimensional spaces. An ensemble is then set up to learn each group with a self-organizing map (SOM). Beside the prediction, these maps also aim at providing a relevant representation of the data which could be used in semi-supervised learning. Finally, the prediction is achieved by a vote of the different maps. Experimentations are performed both in supervised and semi-supervised learning. They show the relevance of this approach.


Random Forest Supervise Learning High Dimensional Space Unlabeled Data Ensemble Approach 
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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© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Elie Prudhomme
    • 1
  • Stéphane Lallich
    • 1
  1. 1.Laboratoire ERICUniversité Lumière Lyon 2BronFrance

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