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

Τύπος:	Άρθρο σε επιστημονικό περιοδικό
Τίτλος:	Information theoretic multi-target feature selection via output space quantization
Συγγραφέας:	[EL] Σεχίδης, Κωνσταντίνος[EN] Sechidis, Konstantinos [EL] Σπυρομήτρος - Ξιούφης, Ελευθέριος[EN] Spyromitros-Xioufis, Eleftherios [EL] Βλαχάβας, Ιωάννης[EN] Vlahavas, Ioannis I.
Ημερομηνία:	31/08/2019
Περίληψη:	A key challenge in information theoretic feature selection is to estimate mutual information expressions that capture three desirable terms—the relevancy of a feature with the output, the redundancy and the complementarity between groups of features. The challenge becomes more pronounced in multi-target problems, where the output space is multi-dimensional. Our work presents an algorithm that captures these three desirable terms and is suitable for the well-known multi-target prediction settings of multi-label/dimensional classification and multivariate regression. We achieve this by combining two ideas—deriving low-order information theoretic approximations for the input space and using quantization algorithms for deriving low-dimensional approximations of the output space. Under the above framework we derive a novel criterion, Group-JMI-Rand, which captures various high-order target interactions. In an extensive experimental study we showed that our suggested criterion achieves competing performance against various other information theoretic feature selection criteria suggested in the literature.
Γλώσσα:	Αγγλικά
Τόπος δημοσίευσης:	Switzerland
Σελίδες:	15
DOI:	https://doi.org/10.3390/e21090855
ISSN:	1099-4300
Θεματική κατηγορία:	[EL] Επιστήμη Ηλεκτρονικών Υπολογιστών και Πληροφορική[EN] Computer and Information Sciences
Λέξεις-κλειδιά:	machine learning; feature selection; multi-target regression; mutual information; multi-label classification; clustering
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/1099-4300/21/9/855
Ηλεκτρονική διεύθυνση του τεκμηρίου στον εκδότη:	https://www.mdpi.com/1099-4300/21/9/855
Ηλεκτρονική διεύθυνση περιοδικού:	https://www.mdpi.com/journal/entropy
Τίτλος πηγής δημοσίευσης:	Entropy
Τεύχος:	9
Τόμος:	21
Σελίδες τεκμηρίου (στην πηγή):	855(1-15)
Σημειώσεις:	This Paper is an Extended Version of Our Paper Published in the 27th European Symposium on Artificial Neural Networks (ESANN), Computational Intelligence and Machine Learning, Bruges (Belgium), 24–26 April 2019 (This article belongs to the Special Issue Information Theoretic Feature Selection Methods for Big Data) 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