Vol. 4 No. 4 (2025): October
RESEARCH ARTICLES

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

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  • Charitha,
  • Pranitha,
  • Junaith Khan,
  • Jaswanth,
  • C Nikitha
Charitha
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
Pranitha
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
Junaith Khan
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
Jaswanth
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
C Nikitha
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
DOI: https://doi.org/10.5281/zenodo.15250298

Published 2025-04-20

Keywords

  • Spam detection,
  • supervised learning,
  • clustering,
  • imbalanced datasets,
  • NSDC-DS,
  • Naïve Bayes SVM,
  • PCA-SGD
    • ...More
    Less

How to Cite

Charitha, Pranitha, Junaith Khan, Jaswanth, & C Nikitha. (2025). Optimizing Spam Filtering on the Social Web of Things with Supervised Sampling Methods. International Journal of Computational Learning & Intelligence, 4(4), 608–618. https://doi.org/10.5281/zenodo.15250298

Copyright (c) 2025 Charitha, Pranitha, Junaith Khan, Jaswanth, C Nikitha

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

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.
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