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Open AccessArticle

An Auto-Adjustable Semi-Supervised Self-Training Algorithm

1
Computer & Informatics Engineering Department, Technological Educational Institute of Western Greece, 263-34 GR Antirion, Greece
2
Department of Mathematics, University of Patras, 265-00 GR Patras, Greece
*
Author to whom correspondence should be addressed.
Algorithms 2018, 11(9), 139; https://doi.org/10.3390/a11090139
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
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Livieris, I.E.; Kanavos, A.; Tampakas, V.; Pintelas, P. An Auto-Adjustable Semi-Supervised Self-Training Algorithm. Algorithms 2018, 11, 139.

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