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Application of bayesian networks for estimation of individual psychological characteristics

Litvinenko Alexander, Mamyrbayev Orken, Litvinenko Natalya, Shayakhmetova Assem, Kotyra Andrzej

Przegląd Elektrotechniczny

2019

R. 95, nr 5

92--97

An accurate qualitative and comprehensive assessment of human potential is one of the most important challenges in any company or collective. We apply Bayesian networks for developing more accurate overall estimations of psychological characteristics of an individual, based on psychological test results, which identify how much an individual possesses a certain trait. Examples of traits could be a stress resistance, the readiness to take a risk, the ability to concentrate on certain complicated work.

Jakościowa oraz kompleksowa ocena potencjału ludzkiego jest jednym z najważniejszych wyzwań dla każdej firmy lub grupy. Sieci Bayesowskie stosowane są do opracowywania dokładniejszych ogólnych oszacowań cech psychologicznych jednostki, w oparciu o wyniki testów psychologicznych, które określają, jak dużo dana osoba posiada pewnych cech.

Evolutionary computation based on Bayesian classifiers

Miquelez T., Bengoetxea E., Larranaga P.

International Journal of Applied Mathematics and Computer Science

2004

Vol. 14, no 3

335-349

Evolutionary computation is a discipline that has been emerging for at least 40 or 50 years. All methods within this discipline are characterized by maintaining a set of possible solutions (individuals) to make them successively evolve to fitter solutions generation after generation. Examples of evolutionary computation paradigms are the broadly known Genetic Algorithms (GAs) and Estimation of Distribution Algorithms (EDAs). This paper contributes to the further development of this discipline by introducing a new evolutionary computation method based on the learning and later simulation of a Bayesian classifier in every generation. In the method we propose, at each iteration the selected group of individuals of the population is divided into different classes depending on their respective fitness value. Afterwards, a Bayesian classifier---either naive Bayes, seminaive Bayes, tree augmented naive Bayes or a similar one---is learned to model the corresponding supervised classification problem. The simulation of the latter Bayesian classifier provides individuals that form the next generation. Experimental results are presented to compare the performance of this new method with different types of EDAs and GAs. The problems chosen for this purpose are combinatorial optimization problems which are commonly used in the literature.

An algorithm for finding most likely explanations in valuation based systems

Wierzchoń S., Kłopotek M.

Control and Cybernetics

2000

Vol. 29, no 4

921-936

A method for finding a number of best explanations in so-called valuation based system is presented. Roughly speaking, the method allows to sort (decreasingly or increasingly) a function of many variables without explicit computation of values of this function. The only condition is that the function be decomposable, i.e. can be expressed as a combination of a number of low-dimensional functions called components. Two cases are considered: the combination operator has an inverse and a more elaborated case when the combination operator has no inverse.