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Selective sampling for combined learning from labelled and unlabelled data.

Gabrys, B. and Petrakieva, L., 2004. Selective sampling for combined learning from labelled and unlabelled data. In: Lotfi, A. and Garibaldi, J.M., eds. Applications and science in soft computing. London: Springer, 139-148.

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paper-Petrakieva_Gabrys_Rasc2002.pdf - Accepted Version


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This paper examines the problem of selecting a suitable subset of data to be labelled when building pattern classifiers from labelled and unlabelled data. The selection of representative set is guided by a clustering information and various options of allocating a number of samples within clusters and their distributions are investigated. The experimental results show that hybrid methods like Semi-supervised clustering with selective sampling can result in building a classifier which requires much less labelled data in order to achieve a comparable classification performance to classifiers built only on the basis of labelled data.

Item Type:Book Section
Series Name:Advances in soft computing
Number of Pages:346
Additional Information:Selected papers from International Conference on Recent Advances in Soft Computing (4th: 2002: Nottingham, England)
Uncontrolled Keywords:Soft computing, Combined Learning, Labelled and Unlabelled Data, Clustering, Selective Sampling
Group:Faculty of Science & Technology
ID Code:3835
Deposited By: Ms MJ Bowden
Deposited On:17 Dec 2007
Last Modified:14 Mar 2022 13:10


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