Self-Train LogitBoost for Semi-supervised Learning

  • Stamatis Karlos
  • Nikos Fazakis
  • Sotiris Kotsiantis
  • Kyriakos Sgarbas
Conference paper

DOI: 10.1007/978-3-319-23983-5_14

Part of the Communications in Computer and Information Science book series (CCIS, volume 517)
Cite this paper as:
Karlos S., Fazakis N., Kotsiantis S., Sgarbas K. (2015) Self-Train LogitBoost for Semi-supervised Learning. In: Iliadis L., Jayne C. (eds) Engineering Applications of Neural Networks. Communications in Computer and Information Science, vol 517. Springer, Cham

Abstract

Semi-supervised classification methods are based on the use of unlabeled data in combination with a smaller set of labeled examples, in order to increase the classification rate compared with the supervised methods, in which the total training is executed only by the usage of labeled data. In this work, a self-train Logitboost algorithm is presented. The self-train process improves the results by using the accurate class probabilities for which the Logitboost regression tree model is more confident at the unlabeled instances. We performed a comparison with other well-known semi-supervised classification methods on standard benchmark datasets and the presented technique had better accuracy in most cases.

Keywords

Semi-supervised learning Logitboost Classification method Labeled and/or unlabeled data 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Stamatis Karlos
    • 1
  • Nikos Fazakis
    • 2
  • Sotiris Kotsiantis
    • 1
  • Kyriakos Sgarbas
    • 2
  1. 1.Department of MathematicsUniversity of PatrasPatrasGreece
  2. 2.Department of Electrical and Computer EngineeringUniversity of PatrasPatrasGreece

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