Efficient E-Mail Spam Detection Strategy Using Genetic Decision Tree Processing with NLP Features

In the modern era, the proliferation of unsolicited emails, commonly known as spam, poses significant challenges to email communication systems. Traditional spam detection methods often struggle to balance detection accuracy and computational efficiency. This paper introduces an efficient e-mail spam detection strategy utilizing a genetic decision tree algorithm. By integrating genetic algorithms with decision tree classifiers, the proposed method optimizes the selection of features and decision rules, leading to improved classification performance. Experimental results demonstrate that this approach achieves higher detection rates and lower false-positive rates compared to conventional techniques, making it a promising solution for real-time spam detection in dynamic email environments.