Diagrams classification algorithm with consequent enlarging of features
In order to solve a number of applied tasks the classifiers for samples of two -dimensional graphic dependen-cies could be useful. The paper discusses a new approach to solving the problem of graphic dependences classification co m-bining it with the segmentation problem. The article also describes an algorithm of interval consequently enlarged features al-location when the classifier works based on neuron-like hierarchical structure. This classifier is based on the idea of growing 
 pyramidal network that is adapted for work with fuzzy objects descriptions. While learning, the classes models are being 
 formed in the classifier hierarchical structure. The models are interpreted to fuzzy expressions (rules) for fuzzy inference sys-tem. The fuzzy expressions reflect the major characteristics of all objects of training set and are presented in a form unde r-standable for an expert. Features enlarging intervals are obtained by analyzing the rules after training the classifier on prima-ry data. The algorithm of interval features automatic generation allows localizing areas with the same values of fuzzy 
 features. This actually results in investigated graphic dependences segmentation on areas with similar structural properties e s-timation. The operational peculiarities of consequentially enlarged features allocation algorithm are discussed in detail on the 
 example of an artificially generated data. The algorithm software implementation is tested on artificially generated data and on real clinical data (recordings of respiratory sounds, voice and electroencephalograms). The paper studies consistently en-larged features application influence on success of the examined data classification results.