KNN-Classification with a Condensed Data Set

A disadvantage of the KNN method is the Large amount of time which is required to classify unknown patterns with a large data set. Because all patterns of the data set must be examined to classify each unknown the computational requirements may make applications prohibitively expensive. Classification time can be significantly decreased if the training set can be reduced to a smaller number of patterns which lie near the decision boundary. Several strategies have been proposed to find an optimum subset with a minimum number of patterns that correctly classifies all pattern sof the original data set. 

The edited KNN-method eliminates patterns that are incorrectly classified by use of the remainder of the data C405 4063. 

Ritter et. al. C2463 described a selective KNN-method to approximate the decision boundary by an optimum subset of patterns. 

A similar algorithm - the reduced nearest neighbour rule - was described by Gates C3823. 

A KNN-classification is almost identical with the interpretation of spectra by a library search. In library search an unknown spectrum (pattern) is compared with all spectra of known compounds collected in a spectral library. A similarity criterion or a dissimilarity criterion (equivalent to a distance measurement) between two spectra must be defined. To find the most similar spectra in the library, this criterion must be calculated for each library spectrum. 

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