| Original language | English |
|---|---|
| Pages (from-to) | 662-672 |
| Journal | The Computer Journal |
| Volume | 47 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published (in print/issue) - 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.