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.
Similar content being viewed by others
References
N. G. Zagoruiko, Applied Methods of the Data and Knowledge Analysis (Novosibirsk, 1999) [in Russian].
A. Blum and T. Mitchell, “Combining Labeled and Unlabeled Data with Co-Training,” in Proc. of the Workshop on Computational Learning Theory (Morgan Kaufmann, 1998), pp. 92–100.
I. A. Borisova, “Calculation of FRiS-Function over Mixed Dastaset in the Task of Generalized Classification,” in Proc. 3rd Int. Conf. on Inductive Modeling (Yevpatoria, 2010), pp. 44–50.
I. A. Borisova and N. G. Zaguruiko, “Function of Rival Similarity in Taxonomy Task,” in Proc. Conf. KONT-2007 (Novosibirsk, 2007), Vol. 2, pp. 67–76.
I. A. Borisova, V. V. Dyubanov, N. G. Zagoruiko, and O. A. Kutnenko, “Use of FRiS-Function for Decision Rule Construction and Attributes Selection (a Task of Combined Type DX), in Proc. Conf. KONT-2007 (Novosibirsk, 2007), Vol. 1, pp. 37–44.
http://archive.ics.uic.edu/ml/dataset/Brest+Cancer+Wisconsin+(Diagnostic)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Borisova, I.A., Zagoruiko, N.G. Feature selection by using the FRiS function in the task of generalized classification. Pattern Recognit. Image Anal. 21, 117–120 (2011). https://doi.org/10.1134/S1054661811020167
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S1054661811020167