A review of machine learning approaches to Spam filtering
By Thiago S. Guzella, Walmir Matos Caminhas. Expert systems with applications, 2009
554 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/66405753664af1895830f147365cc5903388dea1
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Alper Kursat Uysal
- Serkan Günal
- Vadlamani Ravi
- Cary L. Cooper
- B. Shravan Kumar
- V. Varadharajan
- Suhuai Luo
- Ryan D. Turner
In Information Systems
- Alper Kursat Uysal
- Serkan Günal
- Monideepa Tarafdar
- Vadlamani Ravi
- B. Shravan Kumar
- V. Varadharajan
- Ryan D. Turner
- Hamid Reza Shahriari
Citations in the corpus
- A novel probabilistic feature selection method for text classification (2012, background): “…deployed to various domains such as topic detection (Bracewell, et al., 2009; Zeng & Zhang, 2007), spam e-mail filtering (Gunal, et al., 2006; Guzella & Caminhas, 2009; Yu & Xu, 2008), author identification (Cheng, et al., 2011; Efstathios, 2008), and web page classification…”
- A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, background): “However, SMS contains up to 160 characters comprised of languages slags, short text, and Internet abbreviations [335], [369].”
- A model explanation system (2016, background): “Analogous scenarios occur in computer vision [10], credit risk [8], spam detection [6], etc.”
- Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques (2017, background): “…true for the domain of computer security and privacy, which many different applications have been addressed using these techniques (e.g., spam filtering (Guzella and Caminhas 2009; Caruana and Li 2012) and intrusion detection systems (Garcia-Teodoro et al. 2009; Zhou et al. 2010), to name a few).”
- The technostress trifecta ‐ techno eustress, techno distress and design: Theoretical directions and an agenda for research (2019, background): “Yet we see are beginning to see instances in practice, of IS applications such as “e‐mail personal assistants” that prioritize e‐mail (eg, Kokkalis et al., 2013) and machine learning algorithms that filter spam e‐mail (Guzella & Caminhas, 2009).”
- A survey of the applications of text mining in financial domain (2016, background): “Guzella and Caminhas (2009) presented a comprehensive study of spam filtering with data mining techniques.”