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Combination of Active Learning and Semi-Supervised Learning under a Self-Training Scheme

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Wired Communications Lab, Department of Electrical and Computer Engineering, University of Patras, 26504 Achaia, Greece
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Educational Software Development Lab, Department of Mathematics, University of Patras, 26504 Achaia, Greece
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Author to whom correspondence should be addressed.
Entropy 2019, 21(10), 988; https://doi.org/10.3390/e21100988
Received: 15 August 2019 / Revised: 27 September 2019 / Accepted: 9 October 2019 / Published: 10 October 2019
(This article belongs to the Special Issue Theory and Applications of Information Theoretic Machine Learning)
One of the major aspects affecting the performance of the classification algorithms is the amount of labeled data which is available during the training phase. It is widely accepted that the labeling procedure of vast amounts of data is both expensive and time-consuming since it requires the employment of human expertise. For a wide variety of scientific fields, unlabeled examples are easy to collect but hard to handle in a useful manner, thus improving the contained information for a subject dataset. In this context, a variety of learning methods have been studied in the literature aiming to efficiently utilize the vast amounts of unlabeled data during the learning process. The most common approaches tackle problems of this kind by individually applying active learning or semi-supervised learning methods. In this work, a combination of active learning and semi-supervised learning methods is proposed, under a common self-training scheme, in order to efficiently utilize the available unlabeled data. The effective and robust metrics of the entropy and the distribution of probabilities of the unlabeled set, to select the most sufficient unlabeled examples for the augmentation of the initial labeled set, are used. The superiority of the proposed scheme is validated by comparing it against the base approaches of supervised, semi-supervised, and active learning in the wide range of fifty-five benchmark datasets.
Keywords: active learning; semi-supervised learning; self-training; classification; combination of learning methods active learning; semi-supervised learning; self-training; classification; combination of learning methods
MDPI and ACS Style

Fazakis, N.; Kanas, V.G.; Aridas, C.K.; Karlos, S.; Kotsiantis, S. Combination of Active Learning and Semi-Supervised Learning under a Self-Training Scheme. Entropy 2019, 21, 988.

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