Loading...
Loading...

Go to the content (press return)

Unsupervised ensemble classification with correlated decision agents

Author
Cabrera-Bean, Margarita; Pagès-Zamora, A.; Diaz, C.
Type of activity
Journal article
Journal
IEEE signal processing letters
Date of publication
2019-07-01
Volume
26
Number
7
First page
1085
Last page
1089
DOI
10.1109/LSP.2019.2918945
Project funding
Catalan Government (2017 SGR 578- AGAUR).
Coding and Signal Processing for Emerging Wireless Communication and Sensor Networks
Radio techologies for ultra-dense networks in the 5G and beyond (5G&B) era
Repository
http://hdl.handle.net/2117/134945 Open in new window
URL
https://ieeexplore.ieee.org/document/8721508 Open in new window
Abstract
Decision-making procedures when a set of individual binary labels is processed to produce a unique joint decision can be approached modeling the individual labels as multivariate independent Bernoulli random variables. This probabilistic model allows an unsupervised solution using EM-based algorithms, which basically estimate the distribution model parameters and take a joint decision using a Maximum a Posteriori criterion. These methods usually assume that individual decision agents are conditi...
Citation
Cabrera-Bean, M.; Pagès-Zamora, A.; Diaz, C. Unsupervised ensemble classification with correlated decision agents. "IEEE signal processing letters", 1 Juliol 2019, vol. 26, núm. 7, p. 1085-1089.
Keywords
Correlated Bernoulli distribution, Correlated decision agents, Crowdsourcing, Unsupervised ensemble learning
Group of research
SPCOM - Signal Processing and Communications Group

Participants

Attachments