NDLI: Cost-sensitive three-way email spam filtering

Content Provider	Springer Nature Link
Author	Zhou, Bing Yao, Yiyu Luo, Jigang
Copyright Year	2013
Abstract	Email spam filtering is typically treated as a binary classification problem that can be solved by machine learning algorithms. We argue that a three-way decision approach provides a more meaningful way to users for precautionary handling their incoming emails. Three email folders instead of two are produced in a three-way spam filtering system, a suspected folder is added to allow users make further examinations of suspicious emails, thereby reducing the chances of misclassification. Different from existing ternary email spam filtering systems, we focus on two issues that are less studied, that is, the computation of required thresholds to define the three email categories, and the interpretation of the cost-sensitive characteristics of spam filtering. Instead of supplying the thresholds based on intuitive understandings of the levels of tolerance for errors, we systematically calculate the thresholds based on decision-theoretic rough set model. A loss function is interpreted as the costs of making classification decisions. A decision is made for which the overall cost is minimum. Experimental results show that the new approach reduces the error rate of misclassifying a legitimate email to spam and demonstrates a better performance for the cost-sensitivity aspect.
Starting Page	19
Ending Page	45
Page Count	27
File Format	PDF
ISSN	09259902
Journal	Journal of Intelligent Information Systems
Volume Number	42
Issue Number	1
e-ISSN	15737675
Language	English
Publisher	Springer US
Publisher Date	2013-06-09
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Email spam filtering Cost-sensitive learning Ternary classification Three-way decision Naive Bayes classifier Information Storage and Retrieval Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) Business Information Systems Document Preparation and Text Processing
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Information Systems Software Hardware and Architecture

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