Consensus and reliability: the case of two binary classifiers

A.T.J.R. Cobbenhagen (Corresponding author), A. Carè, M.C. Campi, F.A. Ramponi, W.P.M.H. Heemels

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)
18 Downloads (Pure)

Abstract

In this paper we consider the problem of estimating the probability of misclas-sification when consensus is achieved between two binary classifiers that are trained on the same training set. Firstly, it is shown that, under consensus, the probability of misclassification compares favourably with that of the best of the two classifiers. Secondly, we provide accurate, and yet simple to compute, estimates of the probability of consensus and the probability of misclassification under consensus. This paper provides a theoretical basis for these estimates and demonstrates their accuracy by simulation results on a synthetic data set and on a medical data set for breast cancer cell classification.
Original languageEnglish
Pages (from-to)73-78
Number of pages6
JournalIFAC-PapersOnLine
Volume52
Issue number20
DOIs
Publication statusPublished - 2019
Event8th IFAC Workshop on Distributed Estimation and Control in Networked Systems NECSYS 2019 -
Duration: 16 Sep 201917 Sep 2019

Keywords

  • Consensus
  • Classifiers
  • Multi-agent
  • Machine learning
  • Optimisation

Fingerprint Dive into the research topics of 'Consensus and reliability: the case of two binary classifiers'. Together they form a unique fingerprint.

Cite this