The Issue of Efficient Generation of Generalized Features in Algorithmic Classification Tree Methods

The paper studies the basic issue of methods of constructing models of algorithmic classification trees which is the problem of generating generalized features in their structure. There has been suggested a simple and efficient method of approximating the initial training dataset with the help of a set of generalized features of the type of geometric objects in feature space of the current application problem. This approach allows ensuring the necessary accuracy of classification trees, reducing the structural (constructional) complexity and achieving the appropriate performance indicators of the model. Based on the proposed algorithmic scheme of the training set coverage there has been developed the corresponding software which enables the work with a set of different-type applied problems of data processing. Hence, a simple, efficient and economical approximation of the initial training set provides the appropriate speed, level of complexity of the classification scheme, which ensures the simple and complete recognition (coverage) of sets of discrete objects.