Spam Email Filtering Task
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A Spam Email Filtering Task is an email filtering task that is a spam detection task (which requires the detection of spam email in a sequence of emails).
- Context:
- It can range from being a Heuristic Spam Email Filtering Task to being a Data-Driven Spam Email Filtering Task.
- It can be supported by a Spam Email Detection Task.
- …
- Example(s):
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- Counter-Example(s):
- See: Bayes Filter Algorithm, Anomaly Detection, Fraud Detection, Cost-Sensitive Learning; Text Categorization.
References
2017
- (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Anti-spam_techniques Retrieved:2017-11-12.
- Various anti-spam techniques are used to prevent email spam (unsolicited bulk email).
No technique is a complete solution to the spam problem, and each has trade-offs between incorrectly rejecting legitimate email (false positives) vs. not rejecting all spam ([[False positives and false negatives#False negative error|false negatives]]) – and the associated costs in time and effort.
Anti-spam techniques can be broken into four broad categories: those that require actions by individuals, those that can be automated by email administrators, those that can be automated by email senders and those employed by researchers and law enforcement officials.
- Various anti-spam techniques are used to prevent email spam (unsolicited bulk email).
2011
- (Kolcz, 2011) ⇒ Aleksander Kolcz. (2011). “Text Mining for Spam Filtering.” In: (Sammut & Webb, 2011) p.972
- QUOTE: Spam filtering is the process of detecting unsolicited commercial email (UCE) messages on behalf of an individual recipient or a group of recipients. Machine learning applied to this problem is used to create discriminating models based on labeled and unlabeled examples of spam and nonspam. Such models can serve populations of users (e.g., departments, corporations, ISP customers) or they can be personalized to reflect the judgments of an individual. An important aspect of spam detection is the way in which textual information contained in email is extracted and used for the purpose of discrimination.