Quality assessment of data discrimination using self-organizing maps
We introduce two SOM-based methods for data clusterization quality evaluation. Each of these methods can be taken as a basis for the feature selection algorithm. This algorithm is suitable when the classes are separated nonlinearly. The algorithm does not require splitting the data into the training and test sets. The algorithm is not time-consuming.
self-organizing maps feature selection artificial intelligence classification data mining artificial neural networks