А. М. Belevtsev , А.А. Belevtsev , V.А. Balyberdin
47-58
2025-12-30
Abstract ▼
In modern times some foreign countries pay a great attention at the development and using in military field the netcentric conception of control (NCC). The conception defines the architecture of operations as a composition of three network structures: reconnaissance, information control and destruction. The analyses made show that the great part in the network structure of reconnaissance play the radar systems and complexes (RSC). It is pointed out that at present the great efforts are made to realize a new quality RSC on the base of new technologies in nanoelectronics, MEMS/NEMS, nanomaterials, large information networks. That is why the predictions for ways to new technologies constructing of perspective RSC for military objectives is of great interest. The paper deals with some problems connected to the estimations certainty when technological trends and technologies are analyzed. The study is made on the example of radar complexes (RC) in NSS. The procedure for hierarchy criterions system forming is suggested, the bystages priority vector analyses are made on the example of technological trends and technologies developments for sensor greed of NSS. It has been known that the availability of the criterions interrelations reciprocal of the technological trends can made errors for estimations constructed: in vectors priorities estimations; in roadcarts constructing for radar part of NSS. It is recognized that to raise the certainty requires the priorities estimation in the common schematic for technological trends and technologies on the base of analytical networks method.
The quality monitoring system for traditional and distance education requires the development of machine learning classification and quantification techniques necessary to predict individual and collective student performance. This article theoretically and experimentally shows that the most promising approach that simultaneously solves both forecast tasks is to create heterogeneous ensembles consisting of an odd number of different base classifiers, such as decision trees, simple neural networks, naive Bayesian classifier and others. By training and testing 11 different binary classifiers on six different samples of educational data, we show that the individual determined forecast of such ensembles exceeds the accuracy of forecasts of both individual base classifiers and homogeneous ensembles created by bagging and busting technologies. The advantage of heterogeneous ensembles is decisive when we deal with the imbalance of sample characteristic ofeducational data. In these cases, only the forecasts with accuracies exceeding the relative frequency of the class of objects dominating in the sample of data can be considered as useful forecasts. The main advantage of the heterogeneous ensemble is the ability to transform the deterministic forecast into a probabilistic forecast, when instead of referring the object to a particular class, the probability of its belonging to individual classes is given. On this basis, we have proposed a new method of binary quantification, where individual probabilities of belonging to each of the classes of objects are summed up separately, and the resulting total probabilities are interpreted as relative frequencies of objects in the sample. As a result of experiments, it is shown that such ensemble binary quantification is significantly superior to the traditional "classify and count" method.
