som.nn-package: Topological k-NN Classifier Based on Self-Organising Maps

In som.nn: Topological k-NN Classifier Based on Self-Organising Maps

Description

The package som.nn provides tools to train self-organising maps and predict class memberships by means of a k-NN-like classifier.

Details

The functions som.nn.train and som.nn.continue are used train and re-train self-organising maps. The training can be performed with functions of the packages kohonen, som, class or with pure-R-implementations with distance function bubble (kernel internal) or gaussian (kernel gaussian). (Remark: The pure-R-impelementations actually are faster as the external calls to C implementations in the above-mentioned packages!).

In contrast to a normal som training, class lables are required for all training samples. These class lables are used to assign classes to the codebook vectors (i.e. the neurons of the map) after the training and build the set of reference vectors. This reference is used for nearest-neigbour classification.

The nearest neighbour classifier is implemented as predict method. It is controlled by the following parameters:

• dist.fun: the distance function to weight the distance of reference vectors and the sample to be predicted.

• max.dist: the maximum distance to be considered.

Some distance functions are provided in the package (linear, bubble, inverse and tricubic) but any custom function scan be defined as well.

The prediction differs significantly from a standard nearest-neighbour classifier, because the neighbourhood is not defined by the distance between reference vectors and unknown sample vector. Instead the neighbourhood of the neurons on the self-oranising map is used.

Because the som have been generated by an unsupervised training, the classifier is robust against overtraining.

In addition the abstract model can be visualised as 2-dimensional map, using the plot method.

som.nn documentation built on May 2, 2019, 8:26 a.m.