Email classification with co-training

The main problems in text classification are lack of labeled data, as well as the cost of labeling the unlabeled data, which are addressed by exploring co-training - an algorithm that uses unlabeling data along with a few labeled examples to boost the performance of a classifier.

Abstract

The main problems in text classification are lack of labeled data, as well as the cost of labeling the unlabeled data. We address these problems by exploring co-training- an algorithm that uses unlabeled data along with a few labeled examples to boost the performance of a classifier. We experiment with co-training on the email domain. Our results show that the performance of co-training depends on the learning algorithm it uses. In particular, Support Vector Machines significantly outperforms Naive Bayes on email classification.