Behavioral Pattern Identification Through Rough Set Modeling

• Autorzy: Bazan J.G.
• Języki publikacji: EN

Abstrakty

EN

This paper introduces an approach to behavioral pattern identification as a part of a study of temporal patterns in complex dynamical systems. Rough set theory introduced by Zdzisaw Pawlak during the early 1980s provides the foundation for the construction of classifiers relative to what are known as temporal pattern tables. It is quite remarkable that temporal patterns can be treated as features that make it possible to approximate complex concepts. This article introduces what are known as behavior graphs. Temporal concepts approximated by approximate reasoning schemes become nodes in behavioral graphs. In addition, we discuss some rough set tools for perception modeling that are developed for a system for modeling networks of classifiers. Such networks make it possible to recognize behavioral patterns of objects changing over time. They are constructed using an ontology of concepts delivered by experts that engage in approximate reasoning about concepts embedded in such an ontology. We also present a method that we call a method for on-line elimination of non-relevant parts (ENP). This method was developed for on-line elimination of complex object parts that are irrelevant for identifying a given behavioral pattern. The article includes results of experiments that have been performed on data from a vehicular traffic simulator useful in the identification of behavioral patterns by drivers.

Słowa kluczowe

EN

behavior; complex patterns; identification; rough sets; concept approximations; dynamical system

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2006
• Tom: Vol. 72, nr 1-3
• Strony: 37--50
• Opis fizyczny: wykr., bibliogr. 29 poz.

Twórcy

autor

Bazan J.G.

• Institute of Mathematics, University of Rzeszów, Rejtana 16A, 35-959 Rzeszów, Poland,

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