Article1995

A k-nearest neighbor classification rule based on Dempster-Shafer theory

1,021 citations0/0 files storedIEEE Transactions on Systems Man and Cybernetics

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Multi-Criteria Decision Making Data Mining and Machine Learning Applications Rough Sets and Fuzzy Logic

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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2008Classic Works of the Dempster-Shafer Theory of Belief Functions 2016An improved method to construct basic probability assignment based on the confusion matrix for classification problem 2018Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy 2005Environmental impact assessment using the evidential reasoning approach 2002Belief function combination and conflict management 2000The Dempster–Shafer theory of evidence: an alternative approach to multicriteria decision modelling 2000A neural network classifier based on Dempster-Shafer theory 2007Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence

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