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RESEARCH PRODUCT
Improving the k-NCN classification rule through heuristic modifications
Francesc J. FerriJosé Salvador SánchezFiliberto Plasubject
ComputingMethodologies_PATTERNRECOGNITIONTraining setArtificial Intelligencebusiness.industryClassification ruleSignal ProcessingCentroidPattern recognitionComputer Vision and Pattern RecognitionArtificial intelligencebusinessSoftwareMathematicsdescription
Abstract This paper presents an empirical investigation of the recently proposed k-Nearest Centroid Neighbours ( k -NCN) classification rule along with two heuristic modifications of it. These alternatives make use of both proximity and geometrical distribution of the prototypes in the training set in order to estimate the class label of a given sample. The experimental results show that both alternatives give significantly better classification rates than the k -Nearest Neighbours rule, basically due to the properties of the plain k -NCN technique.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 1998-11-01 | Pattern Recognition Letters |