Evolving Systems

, Volume 8, Issue 1, pp 3–18

Locally application of naive Bayes for self-training

  • Stamatis Karlos
  • Nikos Fazakis
  • Angeliki-Panagiota Panagopoulou
  • Sotiris Kotsiantis
  • Kyriakos Sgarbas
Original Paper

DOI: 10.1007/s12530-016-9159-3

Cite this article as:
Karlos, S., Fazakis, N., Panagopoulou, AP. et al. Evolving Systems (2017) 8: 3. doi:10.1007/s12530-016-9159-3

Abstract

Semi-supervised algorithms are well-known for their ability to combine both supervised and unsupervised strategies for optimizing their learning ability under the assumption that only a few examples together with their full feature set are given. In such cases, the use of weak learners as base classifiers is usually preferred, since the iterative behavior of semi-supervised schemes require the building of new temporal models during each new iteration. Locally weighted naïve Bayes classifier is such a classifier that encompasses the power of NB and k-NN algorithms. In this work, we have implemented a self-labeled weighted variant of local learner which uses NB as the base classifier of self-training scheme. We performed an in depth comparison with other well-known semi-supervised classification methods on standard benchmark datasets and we reached to the conclusion that the presented technique had better accuracy in most cases.

Keywords

Naive Bayes classifier Pattern recognition Classification accuracy Labeled/unlabeled data Local decision metrics 

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Stamatis Karlos
    • 1
  • Nikos Fazakis
    • 2
  • Angeliki-Panagiota Panagopoulou
    • 1
  • Sotiris Kotsiantis
    • 3
  • Kyriakos Sgarbas
    • 2
  1. 1.Department of MathematicsUniversity of PatrasPatrasGreece
  2. 2.Wire Communications Laboratory, Department of Electrical and Computer EngineeringUniversity of PatrasPatrasGreece
  3. 3.Educational Software Development Laboratory, Department of MathematicsUniversity of PatrasPatrasGreece