Enhancing the performance of semi-supervised classification algorithms with bridging
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 neighb...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2007
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/7646 https://ink.library.smu.edu.sg/context/sis_research/article/8649/viewcontent/Enhancing_the_performance_of_semi_supervised_classification_algorithms_with_bridging.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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. |
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