Adaptive Approach for Spam Detection
نویسندگان
چکیده
Spam has emerged as a major problem in recent years. The most widely recognized form of spam, is email spam. The accounts which contain spam messages must waste time deleting annoying and possibly offensive message. In this paper, we present a variety of machine learning algorithms to identify spam in e-mail accounts. We design classifier model to automatically determine spam in the accounts so that time of account holder can be saved and utilized on other work. The dataset we used for our project is named as SPAMBASE dataset download from UCI Machine Learning Repository. We used the labeling data in conjunction with machine learning techniques provided by WEKA tool kit, to train a computer to recognize spam instances automatically. The accuracy of 94.28 is shown by the Random committee through the experiment.
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