On Nonlinear Transformations of Features Based on the Functions of Objects Belonging to Classes

The problem of choosing an attribute space for solving classification problems is considered. When choosing a space, nonlinear transformations of the values of both individual features and their combinations are used. The fuzziness of dividing objects into classes according to the values of individual features is investigated using membership functions. The generality of the procedure for calculating the values of the membership function for data measured in nominal and interval scales is shown. The efficiency of the feature space selection is demonstrated through the indicators of the generalizing ability of the algorithms calculated through the estimates of the structure of relations of class objects.

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