The Instance Individual Informativity Evaluation for the Sampling in Neural Network Model Synthesis

The problem of mathematical support development is solved to automate the sampling at diagnostic and recognizing model building by precedents. The object of study is the process of diagnostic and recognizing neural network model building by precedents. The subject of study is the sampling methods for neural network model building by precedents. The purpose of the work is to increase the speed and quality of the formation process of selected training samples for neural network model building by precedents. The method of training sample selection is proposed which for a given initial sample of precedents and given feature space partition determines the weights characterizing the term and feature usefulness. It characterizes the individual absolute and relative informativity of instances relative to the centers and the boundaries of feature intervals based on the weight values. This allows to automate the sample analysis and its division into subsamples, and, as a consequence, to reduce the training data dimensionality. This in turn reduces the time and provides an acceptable accuracy of neural model training. The software implementing proposed indicators is developed. The experiments to study their properties are conducted. The experimental results allow to recommend the proposed indicators for use in practice, as well as to determine effective conditions for the application of the proposed indicators.