Informatica

The study is dictated by the need to interpret and justify the solutions of classification problems. In this context, a method of logical analysis of data is considered along with its modifications based on the specifically developed algorithmic procedures, the use of which can increase the interpretability and generalization capability of classifiers. The article confirms in an empirical way that the suggested optimization models are suitable for building informative patterns and that the designed algorithmic procedures are efficient when used for the method of logical analysis of data.

The problem of post‐processing of a classified image is addressed from the point of view of the Dempster–Shafer theory of evidence. Each neighbour of a pixel being analyzed is considered as an item of evidence supporting particular hypotheses regarding the class label of that pixel. The strength of support is defined as a function of the degree of uncertainty in class label of the neighbour, and the distance between the neighbour and the pixel being considered. A post‐processing window defines the neighbours. Basic belief masses are obtained for each of the neighbours and aggregated according to the rule of orthogonal sum. The final label of the pixel is chosen according to the maximum of the belief function.

This paper considers the technique to construct the general decision rule for the contradictory expert classification of objects which are described with many qualitative attributes. This approach is based on the theory of multiset metric spaces, and allows to classify a collection of multi-attribute objects and define the classification rule which approximates the set of individual sorting rules.

The paper presents new method for sequential classification of the time series observations. Methods and algorithms of sequential recognition are obtained on the basis of the recursive equations for sufficient statistics. These recursive equations allow to construct algorithms of current classification of observable sequences in the rate of entering its values into the on-line operation. Classification algorithms are realized in the form of computer programs, including personal computers. They allow to build multi-channel conveyer computational structures for the sequential recognizers of time series observations.

Influence of projection pursuit on classification errors and estimates of a posteriori probabilities from the sample is considered. Observed random variable is supposed to satisfy a multidimensional Gaussian mixture model. Presented computer simulation results show that for comparatively small sample size classification using projection pursuit algorithm gives better accuracy of estimates of a posteriori probabilities and less classification error.