Optimizing Spam Filtering on the Social Web of Things with Supervised Sampling Methods

The rise of digital communication has led to an increasing challenge in detecting and filtering spam messages, which negatively affect user experience and system performance. Conventional spam detection methods often struggle with imbalanced datasets, reducing their classification effectiveness. This study presents an innovative supervised learning model that integrates Negative Selection Density Clustering with Down sampling (NSDC-DS) and a Naïve Bayes Support Vector Machine (NBSVM) to enhance spam detection accuracy. NSDC-DS improves data balance by clustering based on density similarity, ensuring better representation of minority classes. Additionally, Principal Component Analysis with Stochastic Gradient Descent (PCA-SGD) is employed to optimize feature selection and enhance model performance. Experimental analysis on diverse communication datasets demonstrates that the proposed approach surpasses traditional classifiers in both accuracy and efficiency. The findings confirm that this method offers a reliable and optimized solution for detecting spam messages in online communication platforms.