ИСТИНА

FCA-based classifiers can deal with nonbinary data representation in different ways: use it directly or binarize it. Those algorithms that binarize data use metric information from the initial feature space only as a result of scaling (feature binarization procedure). Metric approach in this area allows one significantly reducing classification refusals number and provides additional information which can be used for classifier training. In this paper we propose an approach which generalizes some of existing FCA classification methods and allows one to modify them. Unlike other algorithms, the proposed classifier model uses initial metric information together with order object-attribute dependencies.