Enhancing the performance of semi-supervised classification algorithms with bridging

• Main Authors: CHAN, Jason Yuk Hin, POON, Josiah, KOPRINSKA, Irena
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2007
• Subjects: Programming Languages and Compilers; Theory and Algorithms
• 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
• Institution: Singapore Management University

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.