Ali, I., Saad, S., Ahmed, S. (2016). Using One-Class SVM with Spam Classification. , 57(1), 501-506.
Inas Ali; Sumaya Saad; Safa Ahmed. "Using One-Class SVM with Spam Classification". , 57, 1, 2016, 501-506.
Ali, I., Saad, S., Ahmed, S. (2016). 'Using One-Class SVM with Spam Classification', , 57(1), pp. 501-506.
Ali, I., Saad, S., Ahmed, S. Using One-Class SVM with Spam Classification. , 2016; 57(1): 501-506.

Using One-Class SVM with Spam Classification

Iraqi Journal of Science
Article 1, Volume 57, Issue 1, 2016, Pages 501-506
Authors
Inas Ali; Sumaya Saad; Safa Ahmed
Abstract
Support Vector Machine (SVM) is supervised machine learning technique which has become a popular technique for e-mail classifiers because its performance improves the accuracy of classification. The proposed method combines gain ratio (GR) which is feature selection method with one-class training SVM to increase the efficiency of the detection process and decrease the cost. The results show high accuracy up to 100% and less error rate with less number of feature to 5 features.
Keywords
gain ratio; Spam; SVM
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