Prototype Generation Using Self-Organizing Maps for Informativeness-Based Classifier

Table 2

Parametrization of algorithms.

Algorithm	Parameters
NN	= 1 and Euclidian distance
SOM	Euclidian distance, batch training, maximum training time equal to 1000, rectangular lattice, and Gaussian neighborhood function with maximum aperture of 1 with decay due to the number of iterations. The SOM map dimension has the square root of the number of dataset objects by two ()
NN	Execution of the NN algorithm with value equal to 7 (best result from [10]) and informative neighbor number equal to 1