Loading...
Loading...

Go to the content (press return)

Blind multiclass ensemble classification

Author
Traganitis, P. A.; Pagès-Zamora, A.; Giannakis, G.B.
Type of activity
Journal article
Journal
IEEE transactions on signal processing
Date of publication
2018-09-15
Volume
66
Number
18
First page
4737
Last page
4752
DOI
https://doi.org/10.1109/TSP.2018.2860562 Open in new window
Project funding
Coding and Signal Processing for Emerging Wireless Communication and Sensor Networks
Red COMONSENS
Repository
http://hdl.handle.net/2117/120513 Open in new window
URL
https://ieeexplore.ieee.org/document/8421667/ Open in new window
Abstract
The rising interest in pattern recognition and data analytics has spurred the development of innovative machine learning algorithms and tools. However, as each algorithm has its strengths and limitations, one is motivated to judiciously fuse multiple algorithms in order to find the “best” performing one, for a given dataset. Ensemble learning aims at such highperformance meta-algorithm, by combining the outputs from multiple algorithms. The present work introduces a blind scheme for learning...
Citation
Traganitis, P. A., Pagès-Zamora, A., Giannakis, G.B. Blind multiclass ensemble classification. "IEEE transactions on signal processing", 2018.
Keywords
Crowdsourcing, Ensemble learning, Multiclass classification, Unsupervised
Group of research
SPCOM - Signal Processing and Communications Group

Participants

Attachments