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

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.
Algorithms 2018, 11(9), 139;
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)
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
Show Figures

Figure 1

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.

AMA Style

Livieris IE, Kanavos A, Tampakas V, Pintelas P. An Auto-Adjustable Semi-Supervised Self-Training Algorithm. Algorithms. 2018; 11(9):139.

Chicago/Turabian Style

Livieris, Ioannis E., Andreas Kanavos, Vassilis Tampakas, and Panagiotis Pintelas. 2018. "An Auto-Adjustable Semi-Supervised Self-Training Algorithm" Algorithms 11, no. 9: 139.

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

Article Access Map by Country/Region

Back to TopTop