Ensemble-Based Text Classification for Spam Detection

This research proposes an ensemble-based approach for spam detection in digital communication, addressing the escalating challenge posed by unsolicited messages, commonly known as spam. The exponential growth of online platforms has necessitated the development of effective information filtering systems to maintain security and efficiency. The proposed approach involves three main components: feature extraction, classifier selection, and decision fusion. The feature extraction techniques is word embedding, are explored to represent text messages effectively. Multiple classifiers, including RNN including LSTM and GRU are evaluated to identify the best performers for spam detection. By employing the ensemble model combines the strengths of individual classifiers to achieve higher accuracy, precision, and recall. The evaluation of the proposed approach utilizes widely accepted metrics on benchmark datasets, ensuring its generalizability and robustness. The experimental results demonstrate that the ensemble-based approach outperforms individual classifiers, offering an efficient solution for combatting spam messages. Integration of this approach into existing spam filtering systems can contribute to improved online communication, user experience, and enhanced cybersecurity, effectively mitigating the impact of spam in the digital landscape.