Enhancing the Performance of Semi-Supervised Classification Algorithms with Bridging

All Papers

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 self-learning. We empirically show that classification performance increases by improving the semi-supervised algorithm's ability to correctly assign labels to previously-unlabelled data.