Knowledge Uncertainty and Composed Classifier
Eva Ocelíková · 2007
The paper deals with the relations between knowledge management, uncertainty and the context evaluation on the background of computer science, artificial intelligence and the new possibilities of information technologies that can help us to carry out the knowledge management strategies. The paper discuss the problem of wide context (temporal, spatial, local, objective, attribute oriented, relation oriented) as a tool to compensate and to decrease the uncertainty of data, classification and analytical process at all process to increase the information value of decision support. The contribution deals with a problem of creating the composed classifier with boosting architecture, whose components are composed of classifiers working with k - NN algorithm (k - th nearest neighbour).