• Publications
  • Influence
A k -Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory
  • T. Denoeux
  • Computer Science
  • Classic Works of the Dempster-Shafer Theory of…
  • 2008
  • 308
  • 54
ECM: An evidential version of the fuzzy c
tl;dr
A new clustering method for object data has been introduced, in the theoretical framework of belief functions. Expand
  • 290
  • 40
  • Open Access
A k-nearest neighbor classification rule based on Dempster-Shafer theory
  • T. Denoeux
  • Computer Science
  • IEEE Trans. Syst. Man Cybern.
  • 1 May 1995
tl;dr
In this paper, the problem of classifying an unseen pattern on the basis of its nearest neighbors in a recorded data set is addressed from the point of view of Dempster-Shafer theory. Expand
  • 675
  • 36
  • Open Access
Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence
tl;dr
In this paper, a new operator, the cautious rule of combination, is introduced. Expand
  • 168
  • 30
  • Open Access
A neural network classifier based on Dempster-Shafer theory
  • T. Denoeux
  • Computer Science
  • IEEE Trans. Syst. Man Cybern. Part A
  • 1 March 2000
tl;dr
A new adaptive pattern classifier based on the Dempster-Shafer theory of evidence is presented. Expand
  • 316
  • 28
  • Open Access
Inferring a possibility distribution from empirical data
tl;dr
We propose a method to characterize the probabilities of the different classes by simultaneous confidence intervals with a given confidence level [email protected]. Expand
  • 102
  • 15
EVCLUS: evidential clustering of proximity data
  • T. Denoeux, M. Masson
  • Computer Science, Medicine
  • IEEE Transactions on Systems, Man, and…
  • 1 February 2004
tl;dr
A new relational clustering method is introduced, based on the Dempster-Shafer theory of belief functions (or evidence theory). Expand
  • 202
  • 14
  • Open Access
Maximum Likelihood Estimation from Uncertain Data in the Belief Function Framework
  • T. Denoeux
  • Computer Science
  • IEEE Transactions on Knowledge and Data…
  • 2013
tl;dr
We consider the problem of parameter estimation in statistical models in the case where data are uncertain and represented as belief functions. Expand
  • 200
  • 14
  • Open Access
An evidence-theoretic k-NN rule with parameter optimization
tl;dr
The paper presents a learning procedure for optimizing the parameters in the evidence-theoretic k-nearest neighbor rule, a pattern classification method based on the Dempster-Shafer theory of belief functions. Expand
  • 267
  • 13
  • Open Access
Constructing belief functions from sample data using multinomial confidence regions
  • T. Denoeux
  • Computer Science
  • Int. J. Approx. Reason.
  • 2006
tl;dr
We tackle the problem of quantifying beliefs held by an agent about the realization of a discrete random variable X with unknown probability distribution PX , having observed a realization of an independent, identically distributed random sample with the same distribution. Expand
  • 59
  • 13
  • Open Access