Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2007
Abstract
Traditional supervised classification algorithms require a large number of labelled examples to perform accurately. Semi-supervised classification algorithms attempt to overcome this major limitation by also using unlabelled examples. Unlabelled examples have also been used to improve nearest neighbour text classification in a method called bridging. In this paper, we propose the use of bridging in a semi-supervised setting. We introduce a new bridging algorithm that can be used as a base classifier in any supervised approach such as co-training or selflearning. We empirically show that classification performance increases by improving the semi-supervised algorithm’s ability to correctly assign labels to previouslyunlabelled data.
Discipline
Programming Languages and Compilers | Theory and Algorithms
Research Areas
Information Systems and Management
Publication
Proceedings of the Twentieth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007
ISBN
9781577353195
City or Country
Key West
Citation
CHAN, Jason Yuk Hin; POON, Josiah; and KOPRINSKA, Irena.
Enhancing the performance of semi-supervised classification algorithms with bridging. (2007). Proceedings of the Twentieth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007.
Available at: https://ink.library.smu.edu.sg/sis_research/7646
Creative Commons License
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