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

被引：1

作者：

Borisova I.A. ^{[1
]}

Zagoruiko N.G. ^{[1
]}

机构：

[1] Sobolev Institute of Mathematics, Siberian Branch, Russian Academy of Sciences, Novosibirsk 630090

来源：

Pattern Recognition and Image Analysis | 2011年 / 21卷 / 2期

关键词：

Feature Selection; Decision Rule; Generalize Classification; Unlabeled Data; Combine Sample;

D O I：

10.1134/S1054661811020167

中图分类号：

学科分类号：

摘要：

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. © 2011 Pleiades Publishing, Ltd.

引用

页码：117 / 120

页数：3

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