paper on k nearest neighbors and D-S theory

[tdenoeux@hds.univ-compiegne.fr]

Announcement:

The following paper to appear in IEEE Transactions on Systems, Man and 
Cybernetics, 25 (05) is available by anonymous ftp:

	ftp ftp.hds.univ-compiegne.fr
	cd /pub/diagnostic
	get knnds.ps.Z
        uncompress knnds.ps


title:  A k-nearest neighbor classification rule based on Dempster-Shafer Theory
author: Thierry Denoeux

ABSTRACT

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. Each neighbor of a sample to be classified is considered
as an item of evidence that supports certain hypotheses regarding the class 
membership of that pattern. The degree of support is defined as a function of 
the distance between the two vectors. The evidence of the k nearest neighbors 
is then pooled by means of Dempster's rule of combination. This approach 
provides a global treatment of such issues as ambiguity and distance rejection,
and imperfect knowledge regarding the class membership of training patterns. 
The effectiveness of this classification scheme as compared to the voting and 
distance-weighted k-NN procedures is demonstrated using several sets of 
simulated and real-world data.  



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| tdenoeux at hds.univ-compiegne.fr  Thierry DENOEUX                        |
|                                 Departement Genie Informatique         |
|                                 Centre de Recherches de Royallieu      |
| tel (+33) 44 23 44 96           Universite de Technologie de Compiegne |
| fax (+33) 44 23 44 77           B.P. 649                               |
|                                 60206 COMPIEGNE CEDEX                  |
|                                 France                                 |
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