Semi-supervised classification using bridging

• Main Authors: CHAN, Jason Yuk Hin, KOPRINSKA, Irena, POON, Josiah
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2008
• Subjects: semi-supervised learning;bridging; Artificial Intelligence and Robotics
• Online Access: https://ink.library.smu.edu.sg/sis_research/7735
• 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 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.