Nearest neighbour classification

D. Coomans and D.L. Massart, Alternative K-nearest neighbour rules in supervised pattern recognition. Part 1 K-nearest neighbour classification by using alternative voting mles. Anal. Chim. Acta, 136, 15-27 (1982). 

The basic idea underlying nearest-neighbour methods is conceptually very simple, and in practice it is mathematically simple to implement. The general method is based on applying the so-called AT-nearest neighbour classification rule, usually referred to as AT-NN. The distance between the pattern vector of... 

This is a common form of the nearest-neighbour classification rule and assigns a new, unclassified object to that group that contains the majority of its nearest neighbours. 

Nearest neighbours, classification, 138 clustering, 103, 107 Neural networks, 147 Noise, 31... 

The preservation of the local distances can be evaluated by using the K-nearest neighbour classification (Chapter 3) and comparing the results for the d-dimensional patterns and for the 2-dimensional representations. 

K-nearest neighbour classification, linear regression, simplex optimization. 

TABLE 13. K-nearest neighbour classification of 13 chemical classes from binary encoded infrared spectra tested with the leave-one-out method. The Taniraoto distance was used because it gave slightly better results than the Hamming distance. P... 

Table 5.2 Nearest-neighbour classification for NMR (12 features) data set (from Kowalski and Bender 1972, copyright (1972) American Chemical Society)...

Table 5.5 Nearest-neighbour classification of glycosides (from Goux and Weber 1993, with permission of Elsevier Science)...

In order to tackle the issues of non-linearity and scaling, a second version of DAISY was recoded from scratch and implemented (Figure 7.1). This new version (NNC/NVD DAISY) was based on nearest-neighbour classification (NNC), a simple, yet very powerful classification scheme first... 

The number of occurrences of a certain substructure in the hitlist is compared with the corresponding number for the library and a probability is derived for the presence of that substructure in the unknown. This classification method is a variant of the well-known -nearest neighbour classification . Each mass spectrum is considered as a point in a multidimensional space the neighbours nearest to the spectrum of the unknown correspond to the most similar reference spectra in library search. If the majority of k neighbours (k is typically between 1 and 10) contain a certain substructure then this substructure is predicted to be present in the unknown. A drawback of this approach is the high computational effort necessary for classifying an unknown because a full library search is required. The performance has been described by Stein (1995, see Further reading section) as sufficient to recommend it for routine use as a first step in structure elucidation . 

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