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A rule-based classification process hierarchically splits email data to classify spam and ham emails based on core features, as demonstrated by Mohamed et al. (2017). The Decision Tree framework for email classification is straightforward and involves several key steps. First, the training and test data are preprocessed using techniques like TF-IDF and SMOTE to enhance data quality. The Decision Tree model is then initialized with the Gini impurity criterion, which measures the impurity of a node. The model is trained on the preprocessed training data and used to predict labels for the test data. Finally, the model's performance is evaluated using metrics such as accuracy, precision, recall, F1-score, and Area Under Curve (AUC), providing a comprehensive assessment of its effectiveness in spam detection.
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IEEE/ICACT20250220 Slide.08
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