Vadim N. Vagin

Problem of knowledge discovery in noisy databases

The problem of information generalization for real data that may contain noisy data is considered. Various models of information noise are presented, and the influence of noise to the algorithms of generalization is discussed. We used the methods of constructing decision trees and forming production rules. The results of the modeling are presented.

Argumentation Approach and Learning Methods in Intelligent Decision Support Systems in the Presence of Inconsistent Data

Abstract This paper contains a description of methods and algorithms for working with inconsistent data in intelligent decision support systems. An argumentation approach and application of rough sets for generalization problems are considered. The methods for finding the conflicts and the generalization algorithm based on rough sets are proposed. Noise models in the generalization algorithm are viewed. Experimental results are introduced. A decision of some problems that are not solvable in classical logics is given.

Methods and algorithms of information generalization in noisy databases

The problem of information generalization with account for the necessity of processing the information stored in real data arrays which may contain noise is considered. Noise models are presented, and a noise effect on the operation of generalization algorithms using the methods of building decision trees and forming production rules is developed. The results of program modeling are brought about.

Methods and Algorithms of Anomaly Searching in Collections of Time Series

One of the basic problems arising under processing temporal dependencies is the analysis of time series. The various approaches to processing of temporal data are considered. The problem of anomaly detection among sets of time series is setting up. The algorithm TS-ADEEP-Multi for anomaly detection in time series sets for the case when the learning set contains examples of several classes is proposed. The method for improving the accuracy of anomaly detection, due to “compression” of these time series is used. Modelling results for anomaly detection in time series are produced.

Inductive inference and argumentation methods in modern intelligent decision support systems

Improvement of the classification quality for the generalization problem is considered. In order to improve the classification models produced by generalization algorithms, it is proposed to use argumentation methods based on defeasible reasoning with justification degrees. New methods and algorithms are proposed, and experimental results on various test data sets are described, including instances with noisy initial data.

Modeling Defeasible Reasoning for Argumentation

This paper contains a description of an argumentation system that uses a defeasible reasoning mechanism. The main idea and the key points are given. Also it contains main algorithms for detecting the conflicts and finding statuses of arguments. Solutions of some problems, which are not solvable in the classical logics, are presented.

Parallel inference in dynamic decision support systems of a semiotic type

The organization of parallel inference in dynamic decision support systems (DDSS) of a semiotic type, oriented towards a solving of ill-formed problems in dynamic applied domains, is considered. As a knowledge representation model, there are used production rules reflecting expert knowledge about a problem domain, an environment and decision making processes. The main concepts and assertions defining possibility and impossibility of parallel executing the production rules are given. Several types of parallelism in an inference process are introduced. The corresponding algorithm of parallel inference is described. Thus, the purpose of this paper is to develop and to research parallel inference methods and procedures that provide efficient processing a large amount of production rules for DDSS of a semiotic type.

Protection of Information in Networks Based on Methods of Machine Learning

The paper considers the possibility of using artificial intelligence methods in information security tasks: methods for generating inductive concepts for analyzing network traffic, as well as methods of argumentation for automated security decision support systems. The approach proposed in the work allows giving quantitative assessments of the quality of the recommendations developed by the system, thereby helping to solve an important task - the task of choosing the way of responding to suspicious activity in the system. Examples of handling dangerous situations occurring in the system are also presented.

Application of Intelligent Data Analysis Methods for Information Security Problems

In this paper, we consider the ideas and approaches to the intelligent data analysis methods in problems of information security. Solving network security problems is a complex task, involving the large number of factors and requiring finding reasonable compromises between maintaining security, the stable work, enhancing operating expenses and functional restrictions of complex information systems. There are considered the ways to apply inductive concept formation methods for analyzing network traffic, as well as argumentation methods for an automated support of security solutions. The proposed approach allows to give numerical assessments of the quality of recommendations developed by the system, thereby helping to solve an important task - the task of choosing the way to react to suspicious activity in the system. In addition, the example of handling the dangerous situations arising in the system is given.

Implementation of the Argumentation System Based on Defeasible Reasoning Theory

This paper considers the software implementation of the argumentation system based on defeasible reasoning. Argumentation is used for finding and solving possible conflicts and inconsistence in data. The implemented software could be used in different intelligent systems, such as expert systems, decision support systems and machine learning. The main accent of this paper is made on the implementation and practical use of the argumentation system. The paper presents the architecture, main algorithms and examples of usage of the implemented system.