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Contribution to Creation of Complex System Macrosituations
Eva Ocelíková and Ladislav Madarász
Department of Cybernetics and Artificial Intelligence, Technical University of Kosice, Letná 9 04120 Kosice, Slovak Republic
Received:July 2, 2002Accepted:August 2, 2002Published:June 20, 2002
Keywords:situation control, macrosituation, attribute, classification, dimension reduction, principal component
Abstract
This paper deals with the creation of multidimensional data classes – macrosituations – by decreasing their dimension. A large number of monitored attributes in examined situations in complex systems often complicates technical realization of classification and extends the time needed for providing a decision. It is possible to decrease the dimension of situations and, simultaneously, to not decrease decision-making quality. The main subject relates to a possible approach – the Principal Component Method. The basis of this method lies in finding a linear transformation of original p-dimensional space of attributes into a new p’-dimensional space of attributes where p’≤p. New attributes, called principal components, arise in a suitable linear combination of original attributes and are sorted in descending order based on their variance.
Cite this article as:E. Ocelíková and L. Madarász, “Contribution to Creation of Complex System Macrosituations,” J. Adv. Comput. Intell. Intell. Inform., Vol.6 No.2, pp. 79-83, 2002.Data files:
