Hierarchical Classifier

  • Igor T. Podolak
  • Sławomir Biel
  • Marcin Bobrowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3911)


Artificial Intelligence (AI) methods are used to build classifiers that give different levels of accuracy and solution explication. The intent of this paper is to provide a way of building a hierarchical classifier composed of several artificial neural networks (ANN’s) organised in a tree-like fashion. Such method of construction allows for partition of the original problem into several sub-problems which can be solved with simpler ANN’s, and be built quicker than a single ANN. As the sub-problems extracted start to be independent of one another, this paves a way to realise the solutions for the individual sub-problems in a parallel fashion. It is observed that incorrect classifications are not random and can be therefore used to find clusters defining sub-problems.


Confusion Matrix Electric Power Consumption Satisfying Accuracy Committee Machine Practical Machine Learning Tool 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Igor T. Podolak
    • 1
  • Sławomir Biel
    • 1
  • Marcin Bobrowski
    • 1
  1. 1.Institute of Computer ScienceJagiellonian UniversityKrakówPoland

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