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Softmax Regression for ECOC Reconstruction

  • Roberto D’Ambrosio
  • Giulio Iannello
  • Paolo Soda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)

Abstract

Classification by binary decomposition is a well-known method to solve multiclass classification tasks since a large number of algorithms were designed for binary classification. Once the polychotomy has been decomposed into several dichotomies, the decisions of binary learners on a test sample are aggregated by a reconstruction rule to set the final multiclass label. In this context, this paper presents a reconstruction rule based on softmax regression which considers the reconstruction task as a new classification problem. To this aim, as second-order features we use both the crisp labels and the reliabilities of binary decisions. Six heterogeneous datasets and three different classification architectures have been used to test our method, whose performance favorably compare with those provided by other three reconstruction rules both in terms of global accuracy and geometric mean of accuracies.

Keywords

Support Vector Machine Minority Class Heterogeneous Dataset Binary Decomposition Soft Label 
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 2013

Authors and Affiliations

  • Roberto D’Ambrosio
    • 1
  • Giulio Iannello
    • 1
  • Paolo Soda
    • 1
  1. 1.Integrated Research CentreUniversitá Campus Bio-Medico di RomaRomeItaly

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