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- W2507633861 abstract "Spam is no more garbage but risk as it includes virus attachments and spyware agents which make the recipients’ system ruined, therefore, there is an emerging need for spam detection. Many spam detection techniques based on machine learning algorithms have been proposed. As the amount of spam has been increased tremendously using bulk mailing tools, spam detection techniques should deal with it. In this paper we have proposed Hybrid classifier Adaptive boost with support vector machine RBF kernel on Spambase dataset. We have also extracted the features first by Principal component analysis. General Terms: Email Spam classification." @default.
- W2507633861 created "2016-09-16" @default.
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- W2507633861 date "2015-06-25" @default.
- W2507633861 modified "2023-09-26" @default.
- W2507633861 title "IMPROVED SPAMBASE DATASET PREDICTION USING SVM RBF KERNEL WITH ADAPTIVE BOOST" @default.
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- W2507633861 doi "https://doi.org/10.15623/ijret.2015.0406064" @default.
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