Spam Classification with Support Vector Machines Using Van der Waerden Rank Score Attention

As email usage expands, spam has become a critical challenge, threatening network security and reducing communication efficiency. Conventional detection methods face persistent limitations: traditional machine learning models often struggle with high-dimensional sparse data, while deep learning requires substantial computational resources. This study introduces a Van der Waerden rank score feature attention-enhanced Support Vector Machine (VWR-Attn-SVM) to address these issues. The method applies Van der Waerden rank transformation to normalize text features, improving robustness against outliers and preserving ordinal relationships. An enhanced attention mechanism further optimizes feature selection through non-linear processing with regularization, highlighting the features most relevant to spam detection. Experiments on the UCI Spambase and Indonesian Spam datasets show that VWR-Attn-SVM outperforms traditional classifiers in accuracy, precision, recall, F1-score, and AUC. By combining high performance with reduced computational cost, the method provides an efficient and interpretable solution for spam classification, with potential extension to other text-based platforms such as messaging and social media.