1. Αποθετήριο Παραδοτέων Υποτρόφων Δράσεων ΕΣΠΑ Τριτοβάθμιας Εκπαίδευσης 2017-2023
2. Όλο το Αποθετήριο
3. Ερευνητικές ομάδες

Τύπος:	Άρθρο σε περιοδικό
Τίτλος:	Combination of active learning and semi-supervised learning under a self-training scheme
Συγγραφέας:	[EL] Φαζάκης, Νικόλαος[EN] Fazakis, Nikos [EL] Κανάς, Βασίλειος[EN] Kanas, Vasileios [EL] Αρίδας, Χρήστος[EN] Aridas, Christos [EL] Κάρλος, Σταμάτης[EN] Karlos, Stamatis [EL] Κωτσιαντής, Σωτήριος[EN] Kotsiantis, Sotiris B. S.B.
Ημερομηνία:	10/10/2019
Περίληψη:	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.
Γλώσσα:	Αγγλικά
Σελίδες:	28
DOI:	10.3390/e21100988
EISSN:	1099-4300
Θεματική κατηγορία:	[EL] Επιστήμη πληροφόρησης[EN] Information science
Λέξεις-κλειδιά:	active learning; semi-supervised learning; self-training; classification; combination of learning methods
Κάτοχος πνευματικών δικαιωμάτων:	© 2019 by the authors. Licensee MDPI
Όροι και προϋποθέσεις δικαιωμάτων:	This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Διατίθεται ανοιχτά στην τοποθεσία:	https://www.mdpi.com/1099-4300/21/10/988
Ηλεκτρονική διεύθυνση του τεκμηρίου στον εκδότη:	https://www.mdpi.com/1099-4300/21/10/988
Ηλεκτρονική διεύθυνση περιοδικού:	https://www.mdpi.com/journal/entropy
Τίτλος πηγής δημοσίευσης:	Entropy
Τεύχος:	10
Τόμος:	21
Σελίδες τεκμηρίου (στην πηγή):	Article no 988
Σημειώσεις:	This research is implemented through the Operational Program Human Resources Development, Education and Lifelong Learning and is co-financed by the European Union (European Social Fund) and Greek national funds
Εμφανίζεται στις συλλογές:	Ερευνητικές ομάδες