Unsupervized Data-Driven Partitioning of Multiclass Problems

  • Hernán C. Ahumada
  • Guillermo L. Grinblat
  • Pablo M. Granitto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6791)


Many classification problems of high technological value are multiclass. In the last years, several improved solutions based on the combination of simple classifiers were introduced. An interesting kind of methods creates a hierarchy of sub-problems by clustering prototypes of each one of the classes, but the solution produced by the clustering stage is heavily influenced by the label’s information. In this work we introduce a new strategy to solve multiclass problems that makes more use of spatial information than other methods. Based on our previous work on imbalanced problems, we construct a hierarchy of subproblems, but opposite to previous developments, based only on spatial information and not using class labels at any time. We consider different clustering methods (either agglomerative or divisive) for this task. We use an SVM for each sub-problem (if needed, because in several cases the clustering method directly gives a subset with samples of a single class). Using publicly available datasets we compare the new method with several previous approaches, finding promising results.


Support Vector Machine Single Linkage Hierarchical Cluster Method Imbalanced Problem Multiclass Problem 
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 2011

Authors and Affiliations

  • Hernán C. Ahumada
    • 1
  • Guillermo L. Grinblat
    • 1
  • Pablo M. Granitto
    • 1
  1. 1.CIFASIS, French Argentine International Center for Information and Systems SciencesUPCAM (France) / UNR–CONICET (Argentina)RosarioArgentina

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