Extended k-Nearest Neighbours based on Evidence Theory

Hui Wang, DA Bell

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)
Original languageEnglish
Pages (from-to)662-672
Number of pages11
JournalThe Computer Journal
Issue number6
Publication statusPublished - 26 Jun 2004

Bibliographical note

Other Details ------------------------------------ Denoeux's evidence theoretic classifier needs to generate multiple mass functions, which are then combined and used for classification. The combination operation is known to be computationally expensive. A novel evidence theoretic classifier is proposed in this paper where only one mass function is needed to compute the class conditional probability for classification. Such a classifier extends the standard majority voting k-nearest neighbour classifier, and is an approximation of the Bayes classifier. This classifier is shown to perform better than the voting and distance weighted k-nearest neighbour classifiers. This work has led to a series of papers on neighbourhood counting methodology.

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