Feature selection by using the FRiS function in the task of generalized classification

Authors:
I. A. Borisova;N. G. Zagoruiko
Affiliations:
Sobolev Institute of Mathematics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia 630090;Sobolev Institute of Mathematics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia 630090
Venue:
Pattern Recognition and Image Analysis
Year:
2011

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Combining labeled and unlabeled data with co-training

COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory

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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.