The Impact of Reliability Evaluation on a Semi-supervised Learning Approach

  • Pasquale Foggia
  • Gennaro Percannella
  • Carlo Sansone
  • Mario Vento
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)


In self-training methods, unlabeled samples are first assigned a provisional label by the classifier, and then used to extend the training set of the classifier itself. For this latter step it is important to choose only the samples whose classification is likely to be correct, according to a suitably defined reliability measure.

In this paper we want to study to what extent the choice of a particular technique for evaluating the classification reliability can affect the learning performance. To this aim, we have compared five different reliability evaluators on four publicly available datasets, analyzing and discussing the obtained results.


Near Neighbor Reliability Evaluation Unlabeled Data Reliability Estimator Unlabeled Sample 
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 2009

Authors and Affiliations

  • Pasquale Foggia
    • 1
  • Gennaro Percannella
    • 1
  • Carlo Sansone
    • 2
  • Mario Vento
    • 1
  1. 1.Dipartimento di Ingegneria dell’Informazione e di Ingegneria ElettricaUniversità di SalernoFisciano (SA)Italy
  2. 2.Dipartimento di Informatica e SistemisticaUniversità degli Studi di Napoli Federico IINapoliItaly

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