Learning naive Bayes classifiers from positive and unlabelled examples with uncertainty
Jiazhen He,
Yang Zhang,
Xue Li and
Peng Shi
International Journal of Systems Science, 2012, vol. 43, issue 10, 1805-1825
Abstract:
Traditional classification algorithms require a large number of labelled examples from all the predefined classes, which is generally difficult and time-consuming to obtain. Furthermore, data uncertainty is prevalent in many real-world applications, such as sensor network, market analysis and medical diagnosis. In this article, we explore the issue of classification on uncertain data when only positive and unlabelled examples are available. We propose an algorithm to build naive Bayes classifier from positive and unlabelled examples with uncertainty. However, the algorithm requires the prior probability of positive class, and it is generally difficult for the user to provide this parameter in practice. Two approaches are proposed to avoid this user-specified parameter. One approach is to use a validation set to search for an appropriate value for this parameter, and the other is to estimate it directly. Our extensive experiments show that the two approaches can basically achieve satisfactory classification performance on uncertain data. In addition, our algorithm exploiting uncertainty in the dataset can potentially achieve better classification performance comparing to traditional naive Bayes which ignores uncertainty when handling uncertain data.
Date: 2012
References: Add references at CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
http://hdl.handle.net/10.1080/00207721.2011.627475 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:tsysxx:v:43:y:2012:i:10:p:1805-1825
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TSYS20
DOI: 10.1080/00207721.2011.627475
Access Statistics for this article
International Journal of Systems Science is currently edited by Visakan Kadirkamanathan
More articles in International Journal of Systems Science from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().