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  • Author: Gaļina Kuļešova x
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Oļegs Užga-Rebrovs and Gaļina Kuļešova

Abstract

During the past years, a rapid growth has been seen in the descriptive approaches to decision choice. As opposed to normative expected utility theory, these approaches are based on the subjective perception of probabilities by the individuals, which takes place in real situations of risky choice. The modelling of this kind of perceptions is made on the basis of probability weighting functions. In cumulative prospect theory, which is the focus of this paper, decision prospect outcome weights are calculated using the obtained probability weights. If the value functions are constructed in the sets of positive and negative outcomes, then, based on the outcome value evaluations and outcome decision weights, generalised evaluations of prospect value are calculated, which are the basis for choosing an optimal prospect.

In cumulative prospect theory, all relevant evaluations are represented in deterministic form. The present research is an attempt to extend classical prospect theory to the cases when the weights of probabilities are given in a fuzzy form.

Open access

Oļegs Užga-Rebrovs and Gaļina Kuļešova

The Evolution of Biclustering Algorithms

Biclustering methods have been initially developed for solving tasks of finding local correlations between expressions of gene subsets in the subsets of conditions. Later on they started to be employed in target marketing for revealing preferences of subsets of customers/buyers over the subsets of products/services. It can be stated with confidence that in the future these methods will find a wide application in other research areas for mining knowledge when initial data are of specific character. This paper provides a short description and analysis of the four well-known biclustering methods in the order of their evolution.

Open access

Oļegs Užga-Rebrovs and Gaļina Kuļešova

Abstract

Fuzzy inference systems are widely used in various areas of human activity. Their most widespread use lies in the field of fuzzy control of technical devices of different kind. Another direction of using fuzzy inference systems is modelling and assessment of different kind of risks under insufficient or missing objective initial data. Fuzzy inference is concluded by the procedure of defuzzification of the resulting fuzzy sets. A large number of techniques for implementing the defuzzification procedure are available nowadays. The paper presents a comparative analysis of some widespread methods of fuzzy set defuzzification, and proposes the most appropriate methods in the context of ecological risk assessment.

Open access

Oleg Uzhga-Rebrov and Galina Kuleshova

Abstract

Probability boxes (p-boxes) are used as a tool for modeling uncertainty regarding probability distributions in the sets of relevant elements (random events, values of the random variable etc.). To combine information produced by two or more p-boxes, Dempster’s rule for belief combination is commonly used. However, there are plenty of other rules for belief combination developed within the theory of evidence. The purpose of this paper is to present and analyze some widespread rules of that kind as well as examine their potentialities regarding combining the information provided by probability boxes.

Open access

Oleg Uzhga-Rebrov and Galina Kuleshova

Abstract

This paper considers different techniques of operating with fuzzy probability estimates of relevant random events in decision making tasks. The recalculation of posterior probabilities of states of nature based on the information provided by indicator events is performed using a fuzzy version of Bayes’ theorem. The choice of an optimal decision is made on the basis of fuzzy expected value maximisation.

Open access

Oleg Uzhga-Rebrov and Galina Kuleshova

Abstract

Different types of uncertainty are widely spread in all areas of human activity. Probabilistic uncertainties are related to the chances of occurrence of random events. To deal with this kind of uncertainty, statistics and probability theory are successfully employed. Another kind of uncertainty, fuzzy uncertainties refer to imprecision and fuzziness of different kinds of measurements. To cope with this kind of uncertainty, the fuzzy set theory is used. This paper addresses widespread approaches to combining probabilistic and fuzzy uncertainties. The theoretical fundamentals of the approaches are considered within the context of the generalized theory of uncertainty (GTU).

Open access

Oleg Uzhga-Rebrov and Galina Kuleshova,

Abstract

Probabilistic estimates are numerical representations of chances of random event occurrence. The classical theory of probability is based on the assumption that probabilistic estimates are deterministic. If available initial data are sufficient, this kind of estimates can be really obtained. However, when such data are not available, probabilistic estimates become uncertain. This paper analyses and compares three widespread approaches to modelling uncertain estimates and provides practical recommendations on their use.

Open access

Pavels Osipovs, Andrejs Rinkevics, Galina Kuleshova and Arkady Borisov

Abstract

This paper examines the possibility of using Markov chains when constructing a profile of author’s writing style. Thus, the constructed profile can be then used to analyze other texts and calculate their level of similarity. The extraction of the unique profile of text writing style that is characteristic of a specific human can be a topical task in many spheres of human activity. As an example, the task of detecting authorship for scientific and fiction texts can be mentioned. The paper describes a basic theoretical apparatus used for profile construction, software implementation of the experimental system as well as the experiments made and provides experimental results and their analysis.