2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) 2011
DOI: 10.1109/cidm.2011.5949301
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Feature extraction for multi-label learning in the domain of email classification

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Cited by 8 publications
(5 citation statements)
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“…The analysis of cyber-attacks is a must for detecting the attacker and preventing their further malicious activities. The digital information security forensics is a time-and resource-consuming process, therefore automation should be used as much as possible to reduce the investigation time as well as to increase its accuracy [4,5]. One of the first steps in the forensics is classification of obtained data and its prioritization.…”
Section: Introductionmentioning
confidence: 99%
“…The analysis of cyber-attacks is a must for detecting the attacker and preventing their further malicious activities. The digital information security forensics is a time-and resource-consuming process, therefore automation should be used as much as possible to reduce the investigation time as well as to increase its accuracy [4,5]. One of the first steps in the forensics is classification of obtained data and its prioritization.…”
Section: Introductionmentioning
confidence: 99%
“…Notwithstanding the flexibility and applicability of neural networks to this problem, they rely on extracted features as the inputs to their decision engine, like all machine learning algorithms. Much research has been done on feature extraction, with some important conclusions contained in [13,14,15,16,4,17,18,19,7,20]. The research discussed in this paper uses a similar approach to that discussed in [10], using a combined set of both statistical and semantic features.…”
Section: Preliminariesmentioning
confidence: 99%
“…Therefore, the model for predicting mass flow by using the feed rate of fuel is not static and it is a cause of concept drift. Another example of concept drift is a spam mail filtering [3,4]. The main source of concept drift in spam mail filtering are the changes in e-mail content and presentation, an adaptive behavior of spammers (may be virtual drift or real drift), and user's attitude that may change the categories of spam and legitimate e-mails (real drift).…”
Section: Introductionmentioning
confidence: 99%