NDLI: Towards Designing an Email Classification System Using Multi-view Based Semi-supervised Learning

Content Provider	IEEE Xplore Digital Library
Author	Li, Wenjuan Meng, Weizhi Tan, Zhiyuan Xiang, Yang
Copyright Year	2014
Abstract	The goal of email classification is to classify user emails into spam and legitimate ones. Many supervised learning algorithms have been invented in this domain to accomplish the task, and these algorithms require a large number of labeled training data. However, data labeling is a labor intensive task and requires in-depth domain knowledge. Thus, only a very small proportion of the data can be labeled in practice. This bottleneck greatly degrades the effectiveness of supervised email classification systems. In order to address this problem, in this work, we first identify some critical issues regarding supervised machine learning-based email classification. Then we propose an effective classification model based on multi-view disagreement-based semi-supervised learning. The motivation behind the attempt of using multi-view and semi-supervised learning is that multi-view can provide richer information for classification, which is often ignored by literature, and semi-supervised learning supplies with the capability of coping with labeled and unlabeled data. In the evaluation, we demonstrate that the multi-view data can improve the email classification than using a single view data, and that the proposed model working with our algorithm can achieve better performance as compared to the existing similar algorithms.
Starting Page	174
Ending Page	181
File Size	220053
Page Count	8
File Format	PDF
ISBN	9781479965137
DOI	10.1109/TrustCom.2014.26
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Support vector machines Machine Learning Applications Supervised learning Multi-View Network Security Semi-Supervised Learning Semisupervised learning Feature extraction Data models Electronic mail Email Classification
Content Type	Text
Resource Type	Article

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