Support vector machines for spam categorization
Harris Druckera b c, Donghui Wud, Vladimir N. Vapnikb
- aAT&T Labs-Research, Red Bank, NJ 07701, USA
- bAT and T Research Laboratories, Red Bank, NJ, USA
- cDepartment of Electronic Engineering, Monmouth University, West Long Branch, NJ, USA
- dRensselaer Polytechnic Institute, Troy, NY, USA
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Abstract
We study the use of support vector machines (SVM's) in classifying e-mail as spam or nonspam by comparing it to three other classification algorithms: Ripper, Rocchio, and boosting decision trees. These four algorithms were tested on two different data sets: one data set where the number of features were constrained to the 1000 best features and another data set where the dimensionality was over 7000. SVM's performed best when using binary features. For both data sets, boosting trees and SVM's had acceptable test performance in terms of accuracy and speed. However, SVM's had significantly less training time.
TL;DRThe use of support vector machines in classifying e-mail as spam or nonspam is studied by comparing it to three other classification algorithms: Ripper, Rocchio, and boosting decision trees, which found SVM's performed best when using binary features. (Semantic Scholar)