Semi-supervised classification using bridging

Publication Type

Conference Proceeding Article

Publication Date

6-2008

Abstract

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.

Keywords

semi-supervised learning;bridging

Discipline

Artificial Intelligence and Robotics

Research Areas

Data Science and Engineering

Publication

20th International-Florida-AI-Research-Society Conference

Issue

3

First Page

580

Last Page

585

Identifier

10.1142/S0218213008003972

Publisher

AAAI Press

City or Country

USA

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