Semi-supervised classification using bridging - CHAN, Jason Yuk Hin (Institutional Knowledge at Singapore Management University, 2008)
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 most semi-supervised approaches. We empirically show that the classification performance of two semi-supervised algorithms, self-learning and co-training, improves with the use of our new bridging algorithm in comparison to using the standard classifier, JRipper. We propose a similarity metric for short texts and also study the performance of self-learning with a number of instance selection heuristics.