Abstract
The task of generalized classification combines three well-known problems of machine learning: recognition, taxonomy, and semi-supervised learning. Usually these problems are examined separately, and for solving each of them, special algorithms are developed. The FRiS-TDR algorithm, based on the function of rival similarity, examines these three problems as special cases of the generalized classification problem and solves all of them. In this paper we show how to choose the sets of informative features in the task of generalized classification. For this purpose the measure of compactness for combined (mixed) dataset is developed. It consists of both objects with known labels (class names) and nonclassified objects.
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