An evaluation of statistical spam filtering techniques

By Le Zhang, Jingbo Zhu, T. Yao. TALIP, 2004

362 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/d773ce069286a8f56a675b867792927103e5036a

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Citations in the corpus

A survey of the applications of text mining in financial domain (2016, methodology): “The performance of the regression model was evaluated with the measures of lift, ROC, precision and recall.”
Spam Filtering Based On The Analysis Of Text Information Embedded Into Images (2006, methodology): “This has lead in recent years to investigate the use of text categorisation techniques based on the machine learning and pattern recognition approaches for e-mail semantic content analysis (see for instance Sahami et al., 1998; Drucker et al., 1999; Graham, 2002; Zhang et al., 2004).”
Fundamentals of Information Studies: Understanding Information and Its Environment, Second Edition (2009, methodology): “Since the spam filtering approaches presented in [1, 32] have been well-established, we consider them to be an ideal choice for performance evaluation against SpamED.”
Classifying Software Changes: Clean or Buggy? (2008, methodology): “Accuracy, recall, precision, and F-value measures are widely used to evaluate classification results [46], [53].”
Click Trajectories: End-to-End Analysis of the Spam Value Chain (2011, background): “Thus, a broad and ongoing literature examines ﬁltering spam email based on a variety of content features (e.g., [2], [19], [43], [57]).”
Towards SMS Spam Filtering: Results under a New Dataset (2013, methodology): “[3], [4], [5], [6], [7], [8], [9], [10], [11]), there are still few studies about SMS spam filtering available in the literature.”
Machine learning for email spam filtering: review, approaches and open research problems (2019, neutral): “PU3 1826 2313 44% 2003 Zhang et al [36]”
A comparison of machine learning techniques for phishing detection (2007, methodology): “[25].”
Hierarchical Label Propagation and Discovery for Machine Generated Email (2016, background): “As a result of these complexities, greylists and machine learning techniques have become the default methods for spam detection, many of which utilize textual analysis methods [21, 3, 25].”
A survey of learning-based techniques of email spam filtering (2008, methodology): “Support Vector Machines B,H,W B,H,W [6, 9, 14, 26, 52, 53, 83, 96, 102, 105]”
A review of machine learning approaches to Spam filtering (2009, methodology): “Zhang et al. (2004) compared the performance of five Spam filters using the SpamAssassin, LingSpam and PU1 corpora, in addition to the Chinese corpus ZH1 compiled by the authors.”
Contributions to the study of SMS spam filtering: new collection and results (2011, methodology): “In addition, we did not perform language-specific preprocessing techniques such as stop word removal or word stemming, since other researchers found that such techniques tend to hurt spam-filtering accuracy [5, 17].”