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

Τύπος:	Ανακοίνωση σε συνέδριο
Τίτλος:	Combining active learning with self-train algorithm for classification of multimodal problems
Συγγραφέας:	[EL] Κάρλος, Σταμάτης[EN] Karlos, Stamatis [EL] Κανάς, Βασίλειος[EN] Kanas, Vasileios [EL] Αρίδας, Χρήστος[EN] Aridas, Christos [EL] Φαζάκης, Νικόλαος[EN] Fazakis, Nikos [EL] Κωτσιαντής, Σωτήριος[EN] Kotsiantis, Sotiris B. S.B.
Ημερομηνία:	15/07/2019
Περίληψη:	In real-world cases, handling of both labeled and unlabeled data has raised the interest of several data scientists and Machine Learning engineers, leading to several demonstrations that apply data augmenting approaches to achieve an effective learning behavior. Although the majority of them propose either the exploitation of Semi-supervised or Active Learning approaches, individually, their combination has not been widely used. The ambition of this strategy is the efficient utilization of the available human knowledge relying along with the decisions driven by automated methods under a common framework. Thus, we conduct an empirical evaluation of such a combinatory approach over three problems, related to multimodal data operating under the pool-based scenario: Gender Identification, Recognition of Offensive Language and Emotion Detection. Into the proposed learning framework, which exploits initially labeled instances with small cardinality, our results prove the benefits of adopting such kind of semi-automated approaches regarding both the achieved predictive correctness and the reduced consumption of time and cost resources, as well as the smoothness of the learning convergence, mainly using ensemble classifiers.
Γλώσσα:	Αγγλικά
Σελίδες:	8
DOI:	10.1007/978-3-030-19823-7_3
ISBN:	978-1-7281-4959-2
Θεματική κατηγορία:	[EL] Επιστήμη πληροφόρησης[EN] Information science
Λέξεις-κλειδιά:	Active self-training framework; semi-supervised learning; Extremely Randomized Trees; data augmentation techniques; semi-automated approaches
Κάτοχος πνευματικών δικαιωμάτων:	© Copyright 2019 IEEE - All rights reserved
Ηλεκτρονική διεύθυνση του τεκμηρίου στον εκδότη:	https://ieeexplore.ieee.org/document/8900724
Τίτλος πηγής δημοσίευσης:	10th International Conference on Information, Intelligence, Systems and Applications (IISA) Proceedings
Σελίδες τεκμηρίου (στην πηγή):	1-8
Σειρά δημοσίευσης:	International Conference on Information, Intelligence, Systems and Applications (IISA)
Όνομα εκδήλωσης:	2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)
Τοποθεσία εκδήλωσης:	Patras, Greece
Ημ/νία έναρξης εκδήλωσης:	15/07/2019
Ημ/νία λήξης εκδήλωσης:	17/07/2019
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