Enhancing email classification using data reduction and disagreement-based semi-supervised learning
Yuxin Meng, Wenjuan Li, Lam For Kwok · 2014
Email classification is an important topic in literature attempting to correctly classify user emails and filter out spam emails. In this paper, we identify some challenges regarding this topic and propose an effective email classification model based on both data reduction and disagreement-based semi-supervised learning. In particular, the main objective of the data reduction is to select an optimum collection of email features and reduce the pointless data, while the objective of the disagreement-based approach is to enhance the accuracy of detecting spam emails by utilizing unlabeled data automatically. In the evaluation, we explore the performance of our proposed email classification model using two public datasets and a private dataset. The experimental results demonstrate that our proposed model can overall enhance the performance of email classification through improving detection accuracy and reducing false rates.