Skip to main content

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.

Full text available as:

[img]
Preview
PDF
paper-Petrakieva_Gabrys_Rasc2002.pdf - Accepted Version

102kB

Official URL: http://www.springer.com/engineering/book/978-3-540...

Abstract

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
ISBN:3540408568
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:15 Aug 2021 08:35

Downloads

Downloads per month over past year

More statistics for this item...
Repository Staff Only -