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Extracting Decision Rules from Linguistic Data Describing Economic Phenomena. The Approach Based on Decision Systems over Ontological Graphs and PSO

100%
Pancerz K., Lewicki A.
Barometr Regionalny. Analizy i prognozy
|
2014
|
issue 1(35)
133-140
EN
The aim of the paper is to present a heuristic method for extracting the most general decision rules from linguistic data describing economic phenomena included in simple decision systems over ontological graphs. Such decision systems have been proposed to deal with linguistic attribute values, describing objects of interest, which are concepts placed in semantic spaces expressed by means of ontological graphs. Ontological graphs deliver some additional knowledge (the so-called background knowledge) about semantic relations between concepts which can be useful in classification processes. As heuristics, we propose to use Particle Swarm Optimization (PSO ), which is reported as a successful method in many applications.
PL
Celem artykułu jest przedstawienie heurystycznej metody ekstrakcji najbardziej ogólnych reguł decyzyjnych z danych lingwistycznych, opisujących zjawiska ekonomiczne, zawartych w systemach decyzyjnych nad grafami ontologicznymi. Systemy decyzyjne tego typu zaproponowane zostały w celu poradzenia sobie z lingwistycznymi wartościami atrybutów opisującymi rozważane obiekty, które są pojęciami umieszczonymi w przestrzeniach semantycznych reprezentowanych przez grafy ontologiczne. Grafy ontologiczne dostarczają nam pewną dodatkową wiedzę (tzw. wiedzę bazową) o relacjach semantycznych pomiędzy pojęciami, która może być pomocna w procesach klasyfikacji. Jako heurystykę zaproponowano optymalizację rojem cząstek (Particle Swarm Optimization ) uważaną za metodę odnoszącą sukces w wielu zastosowaniach.
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A solution method for stochastic multilevel programming problems. A systematic sampling evolutionary approach

75%
GOSHU N. N., KASSA S. M.
Operations Research and Decisions
|
2024
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vol. 34
|
issue 1
149-174
EN
Stochastic multilevel programming is a mathematical programming problem with some given number of hierarchical levels of decentralized decision makers and having some kind of randomness properties in the problem definition. The introduction of some randomness property in its hierarchical structure makes stochastic multilevel problems computationally challenging and expensive. In this article, a systematic sampling evolutionary method is adapted to solve the problem. The solution procedure is based on realization of the random variables and systematic partitioning of each hierarchical level’s decision space for searching an optimal reaction. The search goes sequentially upwards starting from the bottom up through the top hierarchical level problem. The existence of solution and convergence of the solution procedure is shown. The solution procedure is implemented and tested on some selected deterministic test problems from literature. Moreover, the proposed algorithm can be used to solve stochastic multilevel programming problems with additional complexity in their problem definition.
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