Rotation Forest model modification within the email spam classification

Increased use of email in daily transactions for many businesses or general communication due to its cost-effectiveness has made emails vulnerable attacks, including spam. Spam are unsolicited messages that very similar each other and sent multiple recipients randomly. This study analyzes the Rotation Forest model modifies it spam classification problem. Also, aim this is create a better classifier. To improve classifier stability, experiments were carried out on Enron spam, Ling SpamAssasin datasets evaluated accuracy, f-measure, precision, recall.