Book ChapterDOI
A k-nearest neighbor classification rule based on Dempster-Shafer theory
Thierry Denoeux
- Vol. 25, Iss: 5, pp 804-813
TLDR
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 to provide a global treatment of such issues as ambiguity and distance rejection, and imperfect knowledge regarding the class membership of training patterns.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. >read more
Citations
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Classification in the Presence of Label Noise: A Survey
Benoît Frénay,Michel Verleysen +1 more
TL;DR: In this paper, label noise consists of mislabeled instances: no additional information is assumed to be available like e.g., confidences on labels.
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Brain Computer Interfaces, a Review
TL;DR: The state-of-the-art of BCIs are reviewed, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface.
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Some remarks on protein attribute prediction and pseudo amino acid composition.
TL;DR: This review is to discuss each of the five procedures of the introduction of pseudo amino acid composition (PseAAC), its different modes and applications as well as its recent development, particularly in how to use the general formulation of PseAAC to reflect the core and essential features that are deeply hidden in complicated protein sequences.
ReportDOI
Combination of Evidence in Dempster-Shafer Theory
Kari Sentz,Scott Ferson +1 more
TL;DR: This report surveys a number of possible combination rules for Dempster-Shafer structures and provides examples of the implementation of these rules for discrete and interval-valued data.
Journal ArticleDOI
Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms.
Kuo-Chen Chou,Hong-Bin Shen +1 more
TL;DR: This protocol is a step-by-step guide on how to use the Web-server predictors in the Cell-PLoc package, a package of Web servers developed recently by hybridizing the 'higher level' approach with the ab initio approach.
References
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Book
A mathematical theory of evidence
TL;DR: This book develops an alternative to the additive set functions and the rule of conditioning of the Bayesian theory: set functions that need only be what Choquet called "monotone of order of infinity." and Dempster's rule for combining such set functions.
Journal ArticleDOI
Nearest neighbor pattern classification
Thomas M. Cover,Peter E. Hart +1 more
TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
Journal ArticleDOI
Discriminatory Analysis - Nonparametric Discrimination: Consistency Properties
Evelyn Fix,J. L. Hodges +1 more
TL;DR: In this paper, the discrimination problem is defined as follows: e random variable Z, of observed value z, is distributed over some space (say, p-dimensional) either according to distribution F, or according to Distribution G. The problem is to decide, on the basis of z, which of the two distributions Z has.
Journal ArticleDOI
A fuzzy K-nearest neighbor algorithm
TL;DR: The theory of fuzzy sets is introduced into the K-nearest neighbor technique to develop a fuzzy version of the algorithm, and three methods of assigning fuzzy memberships to the labeled samples are proposed.
Journal ArticleDOI
The Distance-Weighted k-Nearest-Neighbor Rule
TL;DR: One such classification rule is described which makes use of a neighbor weighting function for the purpose of assigning a class to an unclassified sample.