Risk of KNN-Classifications

If the magnitudes of the features are not similar a scaling should precede the classification C44, 2423. 

Besides the Euclidean distance other distance measurements (Chapter 2.1.8) have been used for chemical applications of the KNN-method. 

The normalized distance is independent of the number of dimensions C1753 the Hamming and Tanimoto distances are suitable for binary encoded patterns C353, 356, 3573. 

In most chemical applications of the KNN-method, only the first (nearest) neighbour (K=1) was used for classification. For K 1 a simple vot1ng ( one neighbour one vote ) may be applied. The contributions of the neighbours to the voting can also be weighted by the distances (or the squared distances) between the unknown and the neighbours. 

The unknown is grouped into class 1 if the voting result is 

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