| Original language | English |
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| Pages (from-to) | 662-672 |
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| Number of pages | 11 |
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| Journal | The Computer Journal |
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| Volume | 47 |
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| Issue number | 6 |
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| DOIs | |
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| Publication status | Published - 26 Jun 2004 |
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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.