Next Article in Journal
Complexity of Hamiltonian Cycle Reconfiguration
Next Article in Special Issue
LSTM Accelerator for Convolutional Object Identification
Previous Article in Journal
Are Markets Truly Efficient? Experiments Using Deep Learning Algorithms for Market Movement Prediction
Article Menu

Export Article

Open AccessArticle
Algorithms 2018, 11(9), 139;

An Auto-Adjustable Semi-Supervised Self-Training Algorithm

Computer & Informatics Engineering Department, Technological Educational Institute of Western Greece, 263-34 GR Antirion, Greece
Department of Mathematics, University of Patras, 265-00 GR Patras, Greece
Author to whom correspondence should be addressed.
Received: 19 July 2018 / Revised: 23 August 2018 / Accepted: 10 September 2018 / Published: 14 September 2018
(This article belongs to the Special Issue Humanistic Data Mining: Tools and Applications)
Full-Text   |   PDF [325 KB, uploaded 17 September 2018]   |  


Semi-supervised learning algorithms have become a topic of significant research as an alternative to traditional classification methods which exhibit remarkable performance over labeled data but lack the ability to be applied on large amounts of unlabeled data. In this work, we propose a new semi-supervised learning algorithm that dynamically selects the most promising learner for a classification problem from a pool of classifiers based on a self-training philosophy. Our experimental results illustrate that the proposed algorithm outperforms its component semi-supervised learning algorithms in terms of accuracy, leading to more efficient, stable and robust predictive models. View Full-Text
Keywords: semi-supervised learning; self-labeling; self-training; classification semi-supervised learning; self-labeling; self-training; classification

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Livieris, I.E.; Kanavos, A.; Tampakas, V.; Pintelas, P. An Auto-Adjustable Semi-Supervised Self-Training Algorithm. Algorithms 2018, 11, 139.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top