Online EM-Based Ensemble Classification With Correlated Agents

Efendi E., DÜLEK B.

IEEE SIGNAL PROCESSING LETTERS, vol.28, pp.294-298, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 28
  • Publication Date: 2021
  • Doi Number: 10.1109/lsp.2021.3052135
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.294-298
  • Keywords: Signal processing algorithms, Parameter estimation, Maximum likelihood estimation, Correlation, Indexes, Computational complexity, Uncertain systems, Ensemble classification, correlation, online, expectation-maximization, ALGORITHM
  • Hacettepe University Affiliated: Yes


A binary ensemble classification method that sequentially processes the data collected from multiple decision agents in the presence of parameter uncertainties is proposed. Agents are assumed to form correlated groups whose decisions are modeled as multivariate Bernoulli random vectors. The prior probabilities of the binary hypotheses and the corresponding probabilities of the outcomes under each hypothesis are treated as unknown deterministic parameters. Cappe's online Expectation-Maximization algorithm is employed to estimate the parameter values, which are then fed into the ensemble classifier. The proposed technique is shown the reduce the computational complexity while delivering performance close to its offline counterpart, which requires multiple passes over the data. Numerical examples are presented to corroborate the results.